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A four-long noncoding RNA signature predicts survival of hepatocellular carcinoma patients.

ABSTRACT: BACKGROUND:Hepatocellular carcinoma (HCC) is a common neoplasm located in the liver. Accumulating evidence has highlighted that long noncoding RNAs (lncRNAs) are correlated with the survival of HCC patients. This study focuses on finding a lncRNA signature to predict the prognostic risk of HCC patients. METHODS:Statistical and machine learning analyses were conducted to analyze the lncRNA expression data and corresponding clinical data of 180 HCC patients collected from the public online Tanric and The Cancer Genome Atlas (TCGA) databases. RESULTS:From the training dataset, we obtained the four-lncRNA model comprising RP11-495K9.6, RP11-96O20.2, RP11-359K18.3, and LINC00556 which can divide HCC patients into two different groups with significantly different prognosis (n = 90, median 1.81, 95% confidence interval [CI]: 1.50-4.91 vs 8.56 years, 95% CI: 6.96-9.97, log-rank test P < .001). The test dataset confirmed the prognostic ability of the signature (n = 90, median 1.95, 95% CI: 1.14-4.08 vs 5.80 years, 95% CI: 3.11-6.82, log-rank test P = .007). Receiver operating characteristic curve displayed the better prediction efficiency of the four-lncRNA signature than the tumor/node/metastasis stage. Cox analysis showed the four-lncRNA signature was an independent predictor of HCC prognosis. CONCLUSION:The four-lncRNA signature can be used as an independent biomarker for HCC patients to predict the prognostic risk.

SUBMITTER: Jiang H

PROVIDER: S-EPMC7521318 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A four-long noncoding RNA signature predicts survival of hepatocellular carcinoma patients.

Jiang Haitao H Zhao Lianhe L Chen Yunjie Y Sun Liang L

Journal of clinical laboratory analysis 20200530 9

<h4>Background</h4>Hepatocellular carcinoma (HCC) is a common neoplasm located in the liver. Accumulating evidence has highlighted that long noncoding RNAs (lncRNAs) are correlated with the survival of HCC patients. This study focuses on finding a lncRNA signature to predict the prognostic risk of HCC patients.<h4>Methods</h4>Statistical and machine learning analyses were conducted to analyze the lncRNA expression data and corresponding clinical data of 180 HCC patients collected from the public ...[more]

PMID: 32474975

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Project description:BackgroundThe dysregulation of autophagy and immunological processes has been linked to various pathophysiological conditions, including cancer. Most notably, their particular involvement in hepatocellular carcinoma (HCC) is becoming increasingly evident. This has led to the possibility of developing a prognostic signature based on immuno-autophagy-related (IAR) genes. Given that long non-coding RNAs (lncRNAs) also play a special role in HCC, a combined signature utilizing IAR genes and HCC-associated long noncoding RNAs (as IARlncRNA) may potentially help in the clinical scenario.MethodWe used Pearson correlation analysis, Kaplan-Meier survival curves, univariate and multivariate Cox regression, and ROC curves to generate and validate a prognostic immuno-autophagy-related long non-coding RNA (IARlncRNA) signature. The Chi-squared test was utilized to investigate the correlation between the obtained signature and the clinical characteristics. CIBERSORT algorithms and the Wilcoxon rank sum test were applied to investigate the correlation between signature and infiltrating immune cells. GO and KEGG analyses were performed to derived signature-dependent pathways.ResultsHerein, we build an IAR-lncRNA signature (as first in the literature) and demonstrate its prognostic ability in hepatocellular carcinoma. Primarily, we identified three IARlncRNAs (MIR210HG, AC099850.3 and CYTOR) as unfavorable prognostic determinants. The obtained signature predicted the high-risk HCC group with shorter overall survival, and was further associated with clinical characteristics such as tumor grade (t = 10.918, p = 0.001). Additionally, several infiltrating immune cells showed varied fractions between the low-risk group and the high-risk HCC groups in association with the obtained signature. In addition, pathways analysis described by the signature clearly distinguishes both risk groups in HCC.ConclusionsThe immuno-autophagy-related long non-coding RNA (IARlncRNA) signature we established exhibits a prognostic ability in hepatocellular carcinoma. To our knowledge, this is the first attempt in the literature to combine three determinants (immune, autophagy and LnRNAs), thus requiring molecular validation of this obtained signature in clinical samples.

Project description:Background: Long noncoding RNAs (lncRNAs) have been discovered to play a regulatory role in genomic instability (GI), which participates in the carcinogenesis of various cancers, including hepatocellular carcinoma (HCC). We endeavored to establish a GI-derived lncRNA signature (GILncSig) as a potential biomarker and explore its impact on immune infiltration and prognostic significance. Methods: Combining expression and somatic mutation profiles from The Cancer Genome Atlas database, we identified GI-related lncRNAs and conducted functional analyses on co-expressed genes. Based on Cox regression analysis, a GILncSig was established in the training cohort (n = 187), and an independent testing patient cohort (n = 183) was used to validate its predictive ability. Kaplan-Meier method and receiver operating characteristic curves were adopted to evaluate the performance. The correlation between GI and immune infiltration status was investigated based on the CIBERSORT algorithm and single sample gene set enrichment analysis. In addition, a comprehensive nomogram integrating the GILncSig and clinicopathological variables was constructed to efficiently assess HCC patient prognosis in clinical applications. Results: A total of 88 GI-related lncRNAs were screened out and the functional analyses indicated diversified effects on HCC progression. The GILncSig was established using four independent lncRNAs (AC116351.1, ZFPM2-AS1, AC145343.1, and MIR210HG) with significant prognostic value (p < 0.05). Following evaluation with the GILncSig, low-risk patients had significantly better clinical outcomes than high-risk patients in the training cohort (p < 0.001), which was subsequently validated in the independent testing cohort. High-risk group exhibited more immunocyte infiltration including B cells memory, macrophages M0 and neutrophils and higher expression of HLA gene set and immune checkpoint genes. Compared to existing HCC signatures, the GILncSig showed better prognosis predictive performance [area under the curve (AUC) = 0.709]. Furthermore, an integrated nomogram was constructed and validated to efficiently and reliably evaluate HCC patient prognosis (3-years survival AUC = 0.710 and 5-years survival AUC = 0.707). Conclusion: The GILncSig measuring GI and impacting immune infiltration serves as a potential biomarker and independent predictor of HCC patient prognosis. Our results highlight further investigation of GI and HCC molecular mechanisms.

Project description:Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five-gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five-gene signature using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five-gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA-LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high-risk group and histidine metabolism in the low-risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real-time PCR validation. In brief, a five-gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five-gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.

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A four-long noncoding RNA signature predicts survival of hepatocellular carcinoma patients.

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A four-long noncoding RNA signature predicts survival of hepatocellular carcinoma patients.

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