Comprehensive bioinformatics analysis reveals potential lncRNA biomarkers for overall survival in patients with hepatocellular carcinoma: an on-line individual risk calculator based on TCGA cohort

The present study downloaded the original study dataset from The Cancer Genome Atlas (TCGA) database. The download and analysis of the study dataset strictly adhered to the relevant data policies of TCGA database.

The gene expression dataset was downloaded from TCGA database (January 28, 2018, https://​tcga-data.​nci.​nih.​gov/​docs/​publications/​tcga/​). The original gene expression data were generated on illumina HiSeq 2000 RNA Sequencing platform. The download gene expression dataset involved 371 hepatocellular carcinoma samples and 50 normal samples with 60,488 original gene expression values. The lncRNAs descripted in GENCODE Resource database (release 27, mapped to GRCh37, https://​www.​gencodegenes.​org/​) were selected for further study. There were 14,449 lncRNAs included in the present study for further analysis.

The lncRNAs which original expression values < 1 were filtered out from the present study. Then the lncRNA expression values were further standardized through method of Trimmed Mean of M [12]. The criteria of differential gene selection were P value < 0.05 and |log₂fold change| > 2.

There were 376 HCC patients in the clinical dataset from TCGA database. The study endpoint in the current study was overall survival. To avoid the effects of unrelated confounding factors, 20 HCC patients with overall survival less than 1 month were excluded from the present study. Eight patients without lncRNA expression information were excluded from the present study. Finally, there were 348 HCC patients enrolled the final survival analysis (Fig. 1). The study period of The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort was from 2010 to 2015. The maximum value and the minimum value of the overall survival time were 120.7 months and 1.0 month. The missing data were recorded as “NA” in the present study. The mean ± standard deviation of age of HCC patients was 59.5 ± 13.4 years in model group. The mean ± standard deviation of follow-up period was 840 ± 701 days. There were 130 (37.4%) out of 348 HCC patients died in the follow-up period.

We carried out an internal validation to validate the predictive performance of the present prognostic model. The validation dataset was constructed by drawing 348 HCC patients using bootstrap resampling method, which was recommended for internal validation of prognostic model [13, 14].

Continuous variables in the present study were presented as mean ± standard deviation (SD). The t-test or Mann–Whitney U test was performed to compare the differences of continuous variables as appropriate. The Chi-squared test or Fisher’s exact test was performed to compare the differences of categorical variables as appropriate. Time-dependent receiver operating characteristic (ROC) curves and Harrell’s concordance index (C-index) were performed to assess the predictive accuracy of prognostic models. The statistical analyses were carried out by using SPSS Statistics 19.0 (SPSS Inc., an IBM Company) and R software (version 3.4.4). The following R packages, such as “pROC”, “plyr”, “rms”, “survival”, “timeROC “ and “glmnet “, were performed as appropriate in the present study. P < 0.05 was defined as the criteria of statistical significance.

Three hundred and forty-eight HCC patients were eventually included in the final survival analysis. The average age of 348 HCC patients was 59.5 ± 13.4 years and the average overall survival time of 348 HCC patients was 28.0 ± 23.7 months in the current study. One hundred and thirty (37.4%) patients out of 348 HCC patients died within the follow-up period in model group. The comparisons of basic characteristics between model group (Additional file 1) and validation cohort (Additional file 2) were summarized in Table 1. There were no significant differences in terms of basic characteristics between model group and validation cohort.

The differential expression analysis between 371 cancer samples and 50 normal samples was performed by using “edgeR” package. Through “edgeR” package, one thousand and five lncRNAs were identified for further survival analysis. The heat map was presented in Additional file 3: Figure S1 and volcano map was presented in Additional file 4: Figure S2.

The univariate Cox regression analyses were conducted to screen the potential lncRNA predictors for overall survival of HCC patients. Based on the potential lncRNA candidates identified by univariate Cox regression analyses, ten lncRNA predictors for overall survival were finally ascertained through multivariate Cox regression analysis. The relevant model information of ten lncRNA candidates were presented in Table 2. The median values of lncRNA expression values were used as cut-off values to transform the original lncRNA expression values into “1” (as high expression) and “0” (as low expression).

Therefore, a prognostic nomogram (Fig. 2) was built by using the expression values of ten lncRNA predictors: LncRNA risk prediction score = (LINC01559 * 0.771) + (MYLK_AS1 * 0.528) + (RP11_150012.3 * 0.728) − (RP11_92C4.6 * 0.509) − (RASGRF2_AS1 * 0.765) + (LINC01116 * 0.731) + (C2orf48 * 0.563) + (LINC00856 * 0.418) + (LINC02003 * 0.483) + (RP11_363N22.3 * 0.432).

Through the median value of LncRNA risk prediction score, 348 patients in model group were stratified into low risk group (n = 174) and high risk group (n = 174). As shown in Fig. 3a, the overall survival rate of low risk patients was significantly higher than that of high risk patients (P < 0.001). The distribution of LncRNA risk prediction score was presented in Fig. 3b. The overall survival status and overall survival time were presented in Fig. 3c. The Harrell’s concordance index (C-index) of LncRNA risk prediction score was 0.761 (95% CI 0.719–0.803) for overall survival in model group.

Time-dependent receiver operating characteristic curves were drawn to depict the clinical application of LncRNA risk prediction score for OS. The C-indexes of LncRNA risk prediction score were 0.811 (95% CI 0.769–0.853) for 1-year overall survival, 0.814 (95% CI 0.772–0.856) for 3-year overall survival and 0.796 (95% CI 0.754–0.838) for 5-year overall survival respectively (Fig. 4a). There were good agreements between predictive survival probability and actual overall survival percentage in calibration curves for 1-year survival (Fig. 4b), 3-year survival (Fig. 4c) and 5-year survival (Fig. 4d).

A internal validation cohort (n = 348) was drawn by random drawing with replacement method from model cohort (n = 348). The calculating method of LncRNA risk prediction scores for patients in validation cohort was as same as the previous formula of LncRNA risk prediction score in model cohort. Then 348 HCC patients in validation cohort were stratified into low risk group (n = 174) and high risk group (n = 174) through the previous cut-off value in model cohort. The survival curve analysis (Fig. 5a) indicated that the overall survival rate in high risk group was significantly poorer than that in low risk group (P < 0.001). The distribution of LncRNA risk prediction score was presented in Fig. 5b. The survival status and survival time were presented in Fig. 5c. The C-index of LncRNA risk prediction score was 0.745 (95% CI 0.703–0.787) for OS in validation cohort.

In validation cohort, the C-indexes of LncRNA risk prediction score were 0.779 (95% CI 0.737–0.821), 0.828 (95% CI 0.786–0.870) and 0.796 (95% CI 0.754–0.838) for 1-year survival, 3-year survival and 5-year survival respectively (Fig. 6a). There were good agreements between predictive survival probability and actual overall survival percentage in calibration curves for 1-year survival (Fig. 6b), 3-year survival (Fig. 6c) and 5-year survival (Fig. 6d).

Multivariate Cox regression analyses were carried out to explore the independence of LncRNA risk prediction score for OS of HCC patients. The pathological diagnosis was carried out in accordance with the suggestions of the American Joint Committee on Cancer (AJCC). After adjusting the confounding effects of pathological parameters, gender and age, multivariate Cox regression analyses indicated that LncRNA risk prediction score was an independent influence factor for OS of HCC patients (Table 3).

The survival curve analysis of lncRNAs in LncRNA risk prediction score was present in Fig. 7. As shown in Fig. 7, OS was significantly different according to ten lncRNAs in LncRNA risk prediction score (P < 0.001).

Pathological stage was an important influence factor for overall survival of HCC patients. As shown in Fig. 8, OS in high risk group was significantly poorer than that in low risk group in different pathological stages, indicating that the predictive performance of LncRNA risk prediction score for OS was stable and reliable in different pathological stage subgroups.

According to the criteria of P value < 0.05 and |Spearman correlation coefficient| > 0.7, 162 mRNA genes were significantly co-expressed with prognostic lncRNAs included in LncRNA risk prediction score. Functional enrichment analysis was performed through the Database for Annotation, Visualization and Integrated Discovery (DAVID, https://​david.​ncifcrf.​gov/​). Gene ontology (GO) biological process enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis were presented in Fig. 9. Functional enrichment analysis indicated that the co-expressed genes were mainly enriched in mitotic nuclear division, cell division, DNA replication, DNA repair, regulation of cell cycle, DNA-dependent ATPase activity, and ATPase activity.

To explore the predictive performance of LncRNA risk prediction score for OS, a 10-group risk stratification chart was presented in Fig. 10 for model cohort. The discriminative ability of LncRNA risk prediction score for 1 year, 2 year, and 3 year OS were showed in Fig. 10a–c.

We further explored the association between prognostic lncRNAs and tumors of digestive system through MNDR v2.0 database (http://​www.​rna-society.​org/​mndr/​index.​html). MNDR v2.0 database integrated clinical evidences from 14 resources and provided a confidence score for each ncRNA-disease association.

RASGRF2_AS1, LINC00856 and LINC01116 were related with hepatocellular carcinoma (score 0.1097), stomach cancer (score 0.1097), and colorectal cancer (score 0.1097). MYLK_AS1 was related with stomach cancer (score 0.8473). RP11_150O12.3 was related with stomach cancer (score 0.4752). LINC01559 and C2orf48 were related with stomach cancer (score 0.1097).