Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

Hub Genes and Long Noncoding RNAs That Regulates It Associated with the Prognosis of Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis

Objective

Through bioinformatics analysis methods, the public databases GEO and TCGA were used to research mRNA and squamous cell carcinoma of the esophagus, construct a lncRNA-mRNA network, and screen hub genes and lncRNAs related to prognosis.

Method

Download esophageal squamous cell carcinoma-related mRNA and lncRNA datasets GEO and TCGA public datasets, as well as clinical data, use bioinformatic tools to perform gene differential expression analysis on the datasets to obtain differentially expressing mRNA (DEmRNA) and lncRNA (DElncRNA), and plot volcano plots and cluster heatmaps. The differential intersection of differentially expressed DEmRNA and DElncRNA was extracted by Venn diagram and imported into CytoScape software, a regulatory network visualization software, to construct a lncRNA-mRNA network and use cytoHubba and MCODE plug-ins to screen hub genes and key lncRNAs. The DEmRNA in the network was imported into the Gene and Protein Interaction Retrieval Database (STRING), gene-encoded protein-protein interactions (PPI) network maps were created, and the genes in the PPI network maps were submitted to GO functional annotation and pathway enrichment analysis using Kyoto Encyclopedia of Gene Genomes (KEGG) (KEGG). The link between hub gene and prognosis was studied using the clinical data collected by TCGA.

Result

Retrieve the datasets GSE23400 and GSE38129 from the GEO database and the esophageal squamous cell carcinoma-related mRNAs from TCGA databases and then obtain intersection. Differentially regulated genes revealed a correlation of 326 (up) with 191 (down) in terms of the differential intersection; for this study, we need to collect the GSE130078 dataset from GEO, as well as the lncRNAs from TCGA databases that are connected to esophageal squamous cell cancer. There were 184 differentially up- and downregulated genes in the differential intersection. A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3). The extracellular matrix, which is important in protein digestion and absorption, was shown to be the primary site of functional enrichment, as shown by GO/KEGG analysis. Squamous cell carcinoma of the mouth and throat is associated with a poor prognosis because of a change in the extracellular matrix structure caused by specific long noncoding RNA (lncRNA) regulatory upregulation.

Conclusion

For the purpose of predicting the prognosis of cancer of the esophagus, researchers studied the esophageal squamous cell carcinoma-related hub genes and important noncoding RNAs (ncRNAs).

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.

Long Noncoding RNA BCYRN1 Recruits BATF to Promote TM4SF1 Upregulation and Enhance HCC Cell Proliferation and Invasion

Hepatocellular carcinoma (HCC) is a common form of cancer for which a subset of reliable clinical biomarkers has been defined. However, other factors including long noncoding RNAs (lncRNAs) can also regulate HCC development. This study was thus designed to understand how the lncRNA Brain cytoplasmic RNA 1 (BCYRN1) modulates HCC progression. Bioinformatics approaches were used to identify genes, lncRNAs, and transcription factors that were differentially expressed in the context of HCC, after which the relative expression of BCYRN1 in HCC and control tissues was assessed via qPCR. The ability of BCYRN1 to bind the transcription factor BATF was further evaluated in an RNA immunoprecipitation (RIP) assay, while chromatin immunoprecipitation (ChIP) was used to gauge the binding of the TM4SF1 promoter by BATF. Luciferase reporter assays were also used to assess the association between BCYRN1 and the TM4SF1 promoter. Subsequent loss- and gain-of-function assays were then conducted to explore the effects of altering BCYRN1 expression levels on the proliferative, invasive, and migratory activity of HCC cells. BCYRN1 upregulation was associated with poorer clinical outcomes in HCC patients, and knocking down this lncRNA impaired HCC cell migration and invasion. From a mechanistic perspective, BATF was recruited to the TM4SF1 promoter by BCYRN1, and reducing the expression of this lncRNA was sufficient to constrain xenograft tumor growth in mice. These results highlight BCYRN1 as a putative therapeutic target in HCC tumors.

A two-phase comprehensive NSCLC prognostic study identifies lncRNAs with significant main effect and interaction

Long noncoding RNA (lncRNA) are involved in regulating physiological behaviors for various malignant tumors, including non-small-cell lung cancer (NSCLC). However, few studies comprehensively evaluated both lncRNA-lncRNA interaction effects and main effects of lncRNA on overall survival of NSCLC. Hence, we performed a two-phase designed study of lncRNA expression in tumor tissues using 604 NSCLC patients from The Cancer Genome Atlas as the discovery phase and 839 patients from Gene Expression Omnibus as the validation phase. In the discovery phase, we adopted a two-step strategy, Screening before Testing, for dimension reduction and signal detection. These candidate lncRNAs first screened out by the weighted random forest (Ranger), were then tested through the Cox proportional hazards model adjusted for covariates. Significant lncRNAs with either type of effects aforementioned were carried forward into the validation phase to confirm their significances again. As a result, in the discovery phase, 19 lncRNAs were identified by Ranger, among which five lncRNAs and one pair of lncRNA-lncRNA interaction exhibited significant effects (FDR-q ≤ 0.05) main and interaction effects on NSCLC survival, respectively, through Cox model. After the independent validation, we finally observed that one lncRNA (ENSG00000227403.1) with main effect was robustly associated with NSCLC prognosis (HR_discovery = 0.90, P = 1.20 × 10^-3; HR_validation = 0.94, P = 4.11 × 10^-3) and one pair of lncRNAs (ENSG00000267121.4 and ENSG00000272369.1) had significant interaction effect on NSCLC survival (HR_discovery = 1.12, P = 3.07 × 10^-4; HR_validation = 1.11, P = 0.0397). Our comprehensive NSCLC prognostic study of lncRNA provided population-level evidence for further functional study.

Integrated multi-omics analysis of the clinical relevance and potential regulatory mechanisms of splicing factors in hepatocellular carcinoma

Splicing factors (SFs) have been increasingly documented to perturb the genome of cancers. However, little is known about the alterations of SFs in hepatocellular carcinoma (HCC). This study comprehensively delineated the genomic and epigenomic characteristics of 404 SFs in HCC based on the multi-omics data from the Cancer Genome Atlas database. The analysis revealed several clinically relevant SFs that could be effective biomarkers for monitoring the onset and prognosis of HCC (such as, HSPB1, DDX39A, and NELFE, which were the three most significant clinically relevant SFs). Functional enrichment analysis of these indicators showed the enrichment of pathways related to splicing and mRNA processes. Furthermore, the study found that SF copy number variation is common in HCC and could be a typical characteristic of hepato-carcinogenesis; the complex expression regulation of SFs was significantly affected by copy number variant and methylation. Several SFs with significant mutation patterns were identified (such as, RNF213, SF3B1, SPEN, NOVA1, and EEF1A1), and the potential regulatory network of SFs was constructed to identify their potential mechanisms for regulating clinically relevant alternative splicing events. Therefore, this study established a foundation to uncover the broad molecular spectrum of SFs for future functional and therapeutic studies of HCC.

lncRNA LINC00355 Acts as a Novel Biomarker and Promotes Glioma Biological Activities via the Regulation of miR-1225/FNDC3B

Background

Accumulating evidence has implicated long noncoding RNAs (lncRNAs) in glioma progression. Here, we aimed to explore the potential roles of a novel lncRNA, LINC00355, in glioma and to clarify the underlying mechanisms.

Methods

RT-PCR was used to examine the relative expressions of LINC00355 in glioma cell lines and specimen samples. The clinicopathological and prognostic significances of LINC00355 in glioma patients were statistically analyzed. To determine cell activities, CCK-8, clonogenic assays, flow cytometry, migration, and invasion assays were performed. Moreover, the potential mechanisms of LINC00355 were investigated by bioinformatics assays and luciferase reporter assays.

Results

LINC00355 expression was increased in glioma cell lines and specimens, and higher LINC00355 expression predicted advanced clinical progress and reduced overall survival and disease-free survival in glioma patients. Functionally, LINC00355 depletion promoted cell proliferation, invasion, and migration in glioma cells and induced apoptosis of glioma cells, whereas LINC00355 upregulation resulted in the opposite effects in vitro. Mechanistic assays revealed that LINC00355 as a sponge for miR-1225 repressed fibronectin type III domain-containing 3B (FNDC3B) expressions.

Conclusion

Our findings revealed the tumor-promotive roles of LINC00355 in the progression of glioma, indicating that LINC00355 exhibited ceRNA functions via modulating miR-1225/FNDC3B axis.

The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma

Background

lncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs’ clinical significance, and find new targeted treatment strategies.

Methods

The corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature.

Results

Finally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e−10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing.

Conclusion

In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04123-6.

An immune-related seven-lncRNA signature for head and neck squamous cell carcinoma

In this study, we developed a long noncoding RNA (lncRNA)‐based prognostic signature for stratification of patients with head a nd neck squamous cell carcinoma (HNSCC). In total, 493 HNSCC samples obtained from the Cancer Genome Atlas database were divided into training and testing cohorts (3:2 ratio). We identified 3913 immune‐related lncRNAs in the HNSCC training cohort by Pearson correlation analysis; only seven were independently associated with overall survival and were used to develop an immune‐related lncRNA prognostic signature (IRLPS) grouping of HNSCC patients into high‐ and low‐IRLPS subgroups. Univariate and multivariate Cox analyses revealed that low‐IRLPS patients had a better prognosis in all the cohorts, which was retained after stratification by sex, grade, and HPV status. Although the TNM stage was also an independent prognostic factor, the IRLPS had a better discriminability with higher AUC at the 3‐ and 5‐year follow‐ups in all cohorts. Low‐IRLPS samples had more immune cell infiltration and were enriched in immune‐related pathways, while high‐ IRLPS samples were enriched in metabolic pathways. A nomogram constructed including age, TNM stage, and IRLPS showed good calibration. Thus, IRLPS improves the prognostic prediction and also distinguishes different tumor microenvironment (TME) in HNSCC patients.

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Long Noncoding RNAs in Gastrointestinal Cancer: Tumor Suppression Versus Tumor Promotion

Approximately 80% of the human genome harbors biochemical marks of active transcription that its majority transcribes to noncoding RNAs, namely long noncoding RNAs (lncRNAs). LncRNAs are heterogeneous RNA transcripts that regulate critical biological processes such as cell survival and death. They involve in the progression of different cancers by affecting transcriptional and post-transcriptional modifications as well as epigenetic control of numerous tumor suppressors and oncogenes. Recent findings show that aberrant expression of lncRNAs is associated with tumor initiation, progression, invasion, and overall survival of patients with gastrointestinal (GI) cancers. Some lncRNAs play as tumor suppressors in all GI cancers, but others play as tumor promoters. However, some other lncRNAs might function as a tumor suppressor in one GI cancer, but as a tumor promoter in another GI cancer type. This fact highlights possible context dependency of the expression patterns and roles of at least some lncRNAs in GI cancer development and progression. Here, we review the functional relation of lncRNAs involved in the development and progression of GI cancer by focusing on their roles as tumor suppressor and tumor promoter genes.

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.

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.

Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma

Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.

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.

Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma

OBJECTIVE

The tumour microenvironment is one of the significant factors driving the carcinogenesis of Pancreatic adenocarcinoma (PAAD). However, the underlying mechanism of how the tumour microenvironment impacts the prognosis of PAAD is not completely clear.

RESULTS

The transcriptome and clinical data of 182 PAAD program cases were downloaded from the TCGA database. Three hundred thirty-three differentially expressed genes (DEGs) between high and low stromal groups and 314 DEGs between high and low immune score groups were identified using ESTIMATE score. Based on the 203 genes differentially expressed simultaneously in two score-related comparisons, we established an 8-mRNA signature to evaluate the prognosis of PAAD patients. Kaplan-Meier curves showed significantly worse survival for patients with high-risk scores in both the training and validation groups. The risk score was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. By searching the TCGA database, we showed that CA9, CXCL9, and GIMAP7 from the 8-mRNA signature were associated with the infiltration levels of immunocytes by regulating FOXO1 expression in PAAD.

CONCLUSIONS

Unlike traditional methods of screening for differential genes in cancer and healthy tissues, we constructed a novel 8-mRNA signature to predict the prognosis of PAAD patients by applying ESTIMATE scoring to RNA-seq-based transcriptome data. Most importantly, we identified CA9, CXCL9, and GIMAP7 from the above eight genes as regulators of immunocyte infiltration by adjusting the expression of FOXO1 in PAAD. Thus, CA9, CXCL9, and GIMAP7 might be the ideal targets of immune therapy of PAAD.

METHODS

ESTIMATE scoring was used to determine the stromal and immune scores of transcriptome datasets downloaded from the TCGA database. An mRNA-based prognostic signature was built for the training cohort via the LASSO Cox regression model. The signature was verified using a validation cohort. Kaplan-Meier curves and log-rank analysis were used to identify survival differences. Western blot analysis and RT-qPCR analysis were carried out to analyze the expression of specific proteins and mRNAs. IHC was performed to assess the protein levels of Forkhead box-O 1 (FOXO1), Carbonic anhydrase 9 (CA9), C-X-C motif chemokine ligand 9 (CXCL9), and GTPase, IMAP family member 7 (GIMAP7) in the tissue microarray of PAAD.

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.

Comprehensive biological function analysis of lncRNAs in hepatocellular carcinoma

Thousands of long non-coding RNAs (lncRNAs) have been discovered in human genomes by gene chip, next-generation sequencing, and/or other methods in recent years, which represent a significant subset of the universal genes involved in a wide range of biological functions. An abnormal expression of lncRNAs is associated with the growth, invasion, and metastasis of various types of human cancers, including hepatocellular carcinoma (HCC), which is an aggressive, highly malignant, and invasive tumor, and a poor prognosis in China. With a more in-depth understanding of lncRNA research for HCC and the emergence of new molecular-targeted therapies, the diagnosis, treatment, and prognosis of HCC will be considerably improved. Therefore, this review is expected to provide recommendations and directions for future lncRNA research for HCC.

MIR22HG As A Tumor Suppressive lncRNA In HCC: A Comprehensive Analysis Integrating RT-qPCR, mRNA-Seq, And Microarrays

Introduction

MIR22HG has a reported involvement in the tumorigenesis of a variety of cancers, including hepatocellular carcinoma (HCC). However, the exact molecular mechanism of MIR22HG in HCC has not been clarified.

Methods

In the present study, we integrated data from in-house RT-qPCR, RNA-sequencing, microarray, and literature studies to conduct a comprehensive evaluation of the clinico-pathological and prognostic significance of MIR22HG in an extremely large group of HCC samples. We also explored the potential mechanism of MIR22HG in HCC by analyzing the alteration profiles of MIR22HG in HCC to predict transcription factors (TFs) that may interact with MIR22HG and to annotate the biological functions of genes co-expressed with MIR22HG. MIR22HG expression was also compared in HCC nude mice xenografts before and after a treatment with nitidine chloride.

Results

We found that MIR22HG was downregulated in HCC and that this downregulation correlated with the malignant phenotype of HCC. Comprehensive analysis of the prognostic impact of MIR22HG in HCC revealed a beneficial effect of MIR22HG on the survival outcome of HCC patients. Seven cases of MIR22HG deep deletion occurred in 360 of the cancer genome atlas (TCGA) provisional HCC samples. A total of 22 MIR22HG-TF-mRNA triplets in HCC were predicted by the lncRNAmap. Co-expressed genes of MIR22HG, identified by weighted correlation network analysis (WGCNA), mainly participated in the pathways involving osteoclast differentiation, chemokine signaling pathways, and hematopoietic cell lineage. In vivo experiments demonstrated that nitidine chloride could stimulate MIR22HG expression in HCC xenografts.

Conclusion

In summary, MIR22HG may play a tumor-suppressive role in HCC by coordinating with predicted TFs and co-expressed genes, such as NLRP3, CSF1R, SIGLEC10, and ZEB2, or by being controlled by nitidine chloride.

Eight key long non-coding RNAs predict hepatitis virus positive hepatocellular carcinoma as prognostic targets

BACKGROUND

Hepatitis B virus, together with hepatitis C virus, has been recognized as the leading causes of hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) have been suggested in increasing studies to be the potential prognostic factors for HCC. However, the role of combined application of lncRNAs in estimating overall survival (OS) for hepatitis virus positive HCC (VHCC) is uncertain.

AIM

To construct an lncRNA signature related to the OS of VHCC patients to enhance the accuracy of prognosis prediction.

METHODS

The expression patterns of lncRNAs, as well as related clinical data were collected from 149 VHCC patients from The Cancer Genome Atlas database. The R package was adopted to obtain the differentially expressed lncRNAs (DElncRNAs). LncRNAs significantly associated with OS were screened by means of univariate Cox regression analysis, so as to construct a least absolute shrinkage and selection operator (LASSO) model. Subsequently, the constructed lncRNA signature was developed and validated. Afterwards, the prognostic nomogram was established, which combined the as-established lncRNA signature as well as the clinical features. Meanwhile, subgroup analysis stratified by the virus type was also performed. Finally, the above-mentioned lncRNAs were enriched to corresponding pathways according to the markedly co-expressed genes.

RESULTS

A total of 1420 DElncRNAs were identified, among which 406 were significant in univariate Cox regression analysis. LASSO regression confirmed 8 out of the 406 lncRNAs, including AC005722.2, AC107959.3, AL353803.1, AL589182.1, AP000844.2, AP002478.1, FLJ36000, and NPSR1-AS1. Then, the prognostic risk score was calculated. Our results displayed a significant association between the risk model and the OS of VHCC [hazard ratio = 1.94, 95% confidence interval (CI): 1.61-2.34, log-rank P = 2e-10]. The inference tree suggested that the established lncRNA signature was useful in the risk stratification of VHCC. Furthermore, a nomogram was plotted, and the concordance index of internal validation was 0.763 (95%CI: 0.700-0.826). Moreover, the subgroup analysis regarding etiology confirmed this risk model. In addition, the Wnt signaling pathway, angiogenesis, the p53 pathway, and the PI3 kinase pathway were the remarkably enriched pathways.

CONCLUSION

An eight-lncRNA signature has been established to predict the prognosis for VHCC, which contributes to providing a novel foundation for the targeted therapy of VHCC.

Low expression of long noncoding RNA CTC-297N7.9 predicts poor prognosis in patients with hepatocellular carcinoma

BACKGROUND

Long noncoding RNAs (lncRNAs) are reported to play important roles in tumorigenesis of various malignant tumors. However, the clinical significance of aberrant lncRNA expression in hepatocellular carcinoma (HCC) is still elusive.

METHODS

Firstly, a differentially expressed lncRNA CTC-297N7.9 in HCC was detected by analyzing the data from The Cancer Genome Atlas (TCGA). Secondly, the expression level of CTC-297N7.9 was examined in four HCC cell lines and 60 pairs of HCC tissues by polymerase chain reaction (PCR) assay at our center. Thirdly, receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of CTC-297N7.9 for HCC. Correlation and survival analysis of HCC patients from the TCGA and our center were also carried out to assess the predictive value of CTC-297N7.9. Finally, survival prognostic models were established combining lncRNA expression and other clinical parameters.

RESULTS

The expression of CTC-297N7.9 was downregulated in HCC cell lines and HCC tissues. ROC curve revealed its significant diagnostic value in HCC. CTC-297N7.9 expression correlated with serum alpha-fetal protein (AFP), tumor stage, and tumor differentiation. Survival analysis indicated that overall survival (OS) and disease-free survival (DFS) are all positively associated with CTC-297N7.9 expression, especially in patients without viral hepatitis or cirrhosis. Cox regression analysis showed that CTC-297N7.9 expression level is an independent prognostic factor for both OS and DFS in HCC patients. Based on the model, CTC-297N7.9 was observed to be negatively correlated to risk score, indicating its role as a protective factor for HCC.

CONCLUSION

Our study demonstrated that the low expression of CTC-297N7.9 is associated with poor prognosis in HCC patients, suggesting its possible role as a potential prognostic marker for HCC.

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC).

Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes.

Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively.

Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.

Identification of aberrantly expressed lncRNA and the associated TF-mRNA network in hepatocellular carcinoma

Emerging evidence has indicated that long noncoding RNA (lncRNA) plays crucial roles in regulating hepatocellular carcinoma (HCC) progression. However, a comprehensive lncRNA-transcription factor (TF)-message RNA (mRNA) in HCC remains absent. The aim of this study was to identify aberrantly expressed lncRNAs and the associated TF-mRNA network in HCC. GSE124535 dataset was downloaded from the Gene Expression Omnibus database. Limma package in R was conducted to analyze abnormally expressed lncRNAs (differentially expressed lncRNAs [DELs]) and mRNAs (differentially expressed genes). The prognostic roles of screened DELs in HCC were evaluated at GEPIA. The expression of DELs that correlated with overall survival of HCC patients was further validated using circlncRNAnet, GEPIA, and StarBase. LncRNA-TF-mRNA potential triplets in HCC were predicted by LncMAP. After that, lncRNA-TF-mRNA networks were established using Cytoscape. A total of 20 upregulated and 17 downregulated DELs were identified in HCC tissues compared with adjacent noncancerous tissues. Six out of 20 upregulated and 6 out of 17 downregulated DELs were identified have significant impact on the overall survival of HCC patients. Results in circlncRNAnet, GEPIA, and Starbase confirmed the expression level of DELs obtained from GSE124535. Finally, the LncRNA-TF-mRNA regulatory networks for upregulated and downregulated lncRNA were established based on these analyses results. Among these lncRNAs in the network, the aberrantly expression of lncRNAs including LINC00511, RP11-290F5.1, MIR4435-2HG, and CTC-537E7.3 in HCC was first time reported to date. In the study, potential LncRNA-TF-mRNA regulatory networks were identified, which will advance our understanding concerning the progression of HCC.

A five lncRNA signature for prognosis prediction in hepatocellular carcinoma

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

Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

The current prognostic long noncoding RNA (lncRNA) signatures for hepatocellular carcinoma (HCC) are still controversial and need to be optimized by systematic bioinformatics analyses with suitable methods and appropriate patients. Therefore, we performed the study to establish a credible lncRNA signature for HCC outcome prediction and explore the related mechanisms. Based on the lncRNA profile and the clinical data of carefully selected HCC patients (n = 164) in TCGA, six of 12727 lncRNAs, MIR22HG, CTC‐297N7.9, CTD‐2139B15.2, RP11‐589N15.2, RP11‐343N15.5, and RP11‐479G22.8 were identified as the independent predictors of patients’ overall survival in HCC by sequential univariate Cox and 1000 times Cox LASSO regression with 10‐fold CV, and multivariate Cox analysis with 1000 times bootstrapping. In the Kaplan‐Meier analysis with patients trichotomized by the six‐lncRNA signature, high‐risk patients showed significantly shorter survival than mid‐ and low‐risk patients (log‐rank test P < 0.0001). According to the ROCs, the six‐lncRNA signature showed superior predictive capacity than the two existing four‐lncRNA combinations and the traditional prognostic clinicopathological parameter TNM stage. Furthermore, low MIR22HG and CTC‐297N7.9, but high CTD‐2139B15.2, RP11‐589N15.2, RP11‐343N15.5, and RP11‐479G22.8, were, respectively, demonstrated to be related with the malignant phenotypes of HCC. Functionally, the six lncRNAs were disclosed to involve in the regulation of multiple cell cycle and stress response‐related pathways via mediating transcription regulation and chromatin modification. In conclusion, our study identified a novel six‐lncRNA signature for resectable HCC prognosis prediction and indicated the underlying mechanisms of HCC progression and the potential functions of the six lncRNAs awaiting further elucidation.