Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Li M

PROVIDER: S-EPMC7752774 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

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

Li Muqi M Liang Minni M Lan Tian T Wu Xiwen X Xie Wenxuan W Wang Tielong T Chen Zhitao Z Shen Shunli S Peng Baogang B

Frontiers in molecular biosciences 20201208

<h4>Background</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 33364253

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Project description:Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

Project description:Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC. Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness. Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs ( LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8+ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group. Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.

Project description:Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions.One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan-Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions.Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways.This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.

Project description:Background: Cellular senescence plays a complicated and vital role in cancer development because of its divergent effects on tumorigenicity. However, the long non-coding RNAs (lncRNAs) associated with tumor senescence and their prognostic value in hepatocellular carcinoma (HCC) remain unexplored. Methods: The trans-cancer oncogene-induced senescence (OIS) signature was determined by gene set variation analysis (GSVA) in the cancer genome atlas (TCGA) dataset. The OIS-related lncRNAs were identified by correlation analyses. Cox regression analyses were used to screen lncRNAs associated with prognosis, and an optimal predictive model was created by regression analysis of the least absolute shrinkage and selection operator (LASSO). The performance of the model was evaluated by Kaplan-Meier survival analyses, nomograms, stratified survival analyses, and receiver operating characteristic curve (ROC) analyses. Gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) were carried out to explore the functional relevance and immune cell infiltration, respectively. Results: Firstly, we examined the pan-cancer OIS signature, and found several types of cancer with OIS strongly associated with the survival of patients, including HCC. Subsequently, based on the OIS signature, we identified 76 OIS-related lncRNAs with prognostic values in HCC. We then established an optimal prognostic model based on 11 (including NRAV, AC015908.3, MIR100HG, AL365203.2, AC009005.1, SNHG3, LINC01138, AC090192.2, AC008622.2, AL139423.1, and AC026356.1) of these lncRNAs by LASSO-Cox regression analysis. It was then confirmed that the risk score was an independent and potential risk indicator for overall survival (OS) (HR [95% CI] = 4.90 [2.74-8.70], p < 0.001), which outperforms those traditional clinicopathological factors. Furthermore, patients with higher risk scores also showed more advanced levels of a proinflammatory senescence-associated secretory phenotype (SASP), higher infiltration of regulatory T (Treg) cells and lower infiltration of naïve B cells, suggesting the regulatory effects of OIS on immune microenvironment. Additionally, we identified NRAV as a representative OIS-related lncRNA, which is over-expressed in HCC tumors mainly driven by DNA hypomethylation. Conclusion: Based on 11 OIS-related lncRNAs, we established a promising prognostic predictor for HCC patients, and highlighted the potential immune microenvironment-modulatory roles of OIS in HCC, providing a broad molecular perspective of tumor senescence.

Project description:BackgroundCancer-associated metabolic reprogramming promotes cancer cell differentiation, growth, and influences the tumor immune microenvironment (TIME) to promote hepatocellular carcinoma (HCC) progression. However, the clinical significance of metabolism-related lncRNA remains largely unexplored.MethodsBased on The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset, we identified characteristic prognostic long non-coding RNAs (lncRNAs) and construct metabolism-related lncRNA prognostic signature for HCC. Gender, age, grade, stage and TP53 status were used as covariates were used to assess the prognostic capacity of the characteristic lncRNA signature. Subsequently, the molecular and immune characteristics and drug sensitivity in metabolism-related lncRNA signature defined subgroups were analyzed.ResultsWe identified 34 metabolism-related lncRNAs significantly associated with the prognosis of HCC (P<0.05). Subsequently, we constructed a multigene signature based on 9 characteristics prognostic lncRNAs and classified HCC patients into high- and low-risk groups based on cutoff values. We found the lncRNA signature [hazard ratio (HR) =3.55 (2.44-5.15), P<0.001] to be significantly associated with survival. The receiver operating characteristic curve (ROC) curves area under the curve (AUC) values for 1-, 3-, and 5-year survival were 0.811, 0.773, and 0.753, respectively. In univariate and multivariate Cox regression analyses, prognostic characteristic lncRNAs were the most crucial prognostic factor besides the stage. The prognostic signature was subsequently validated in the test set. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses revealed potential biological features and signaling pathways associated with the prognostic signature. We constructed a nomogram including risk groups and clinical parameters (age, gender, grade, and stage). Calibration plots and decision curve analysis (DCA) showed that our nomogram had a good predictive performance. Finally, we found reduced expression of immune-activated cells in the high-risk group.ConclusionsThe metabolism-related lncRNA signature is a promising biomarker to distinguish the prognosis and an immune characteristic in HCC.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

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