Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is the most common primary malignancy in the liver and the seventh most frequent neoplasm worldwide [1]. With more than 700,000 death in year 2018, HCC is considered the third leading cause of cancer-related death [2]. The current treatment of HCC involves some complex decision-making processes clinically such as resection, ablation and etc [3]. Hence, optimized methods of diagnosis and prognosis prediction for the diseases became essential for obtaining a better outcome. Alpha-fetoprotein (AFP) and protein induced by vitamin K antagonist-II (PIVKA-II) are widely appreciated as diagnostic biomarkers; however, a highly effective, universal gene panel for HCC prognosis prediction is yet to be widely adopted [4, 5].

In current clinical practice, the outcome of HCC patients was mainly assessed by models based on tumor pathological characters. The Barcelona Clinic Liver Cancer (BCLC) classification is the most used and verified system for HCC with estimated median survival periods at each tumor stage [6]. Other staging systems proposed by Italian, Japanese and Hong Kong scholars provide alternatives with comparable or enhanced accuracy as BCLC, but further prospective study for validation is needed [7–9].

Nevertheless, all these fail to incorporate molecular markers as prognostic predictive factors. Generally, biological markers are viewed as a pivotal indicator for tumor diagnosis, therapeutic effectiveness, and public tumor surveillance. Not surprisingly, a considerable amount of effort has also been made in developing novel biomarkers or signatures for HCC prognosis over the last decade. For instance, Long et al has developed a four-gene (CENPA, SPP1, MAGEB6, HOXD9) based model to predict the overall survival of HCC patients [10]. Liu et al has also established a four-gene (ACAT1, GOT2, PTDSS2, UCK2) based signature but with genes only in metabolic activity [11]. Besides gene expression, Yang et al has identified the TP53 mutation status also serves as a prognosis indicator for HCC [12].

Despite a great number of researches have done on such genomic indexes, most of them focus on protein coding region and their effect on the patients / disease. To date, the value of non-coding RNAs in HCC prognostic assessment has not been thoroughly explored. In recent years, growing evidence has indicated the crucial role of lncRNA in multiple stages of HCC development including genesis, progression, and recurrence [13–15]. These findings have strongly implied the remarkable potential of lncRNA being the next prognostic indicator for better staging and monitoring of HCC.

Therefore, this study aims to develop a lncRNA-based tool to monitor and predict the outcomes of HCC. In detail, the HCC cohort containing lncRNA expression and clinical data was acquired from public databases. Two lncRNA-based signatures: an 8-lncRNA contained classifier for overall survival (OS) prediction and a 6-lncRNA contained classifier for relapse-free survival (RFS) prediction, were constructed by applying COX and LASSO regression with differentially expressed lncRNAs (DElncRNAs). Subsequently, the ability as prognostic predictors of both classifiers was evaluated and compared with traditional staging systems. Last but not least, as the cohort was divided into high- and low-risk groups according to the risk score determined by the signature classifiers, potential therapeutic targets and small molecules for high-risk, poor prognosis-associated patients were explored with methods described below (Figure 1).

Figure 1. The scheme of the study indicates the major steps of building the lncRNA-based classifiers and following evaluation.

Results

Identification of candidate prognostic lncRNAs

The comprehensive RNA expression profile containing tumor tissue (n = 369) and adjacent control (n = 50) was accessed from the TCGA database as previously described. Of the 14089 lncRNAs extracted from the RNA-seq data, 1318 lncRNAs were identified as DElncRNA under the condition of |logFC| > 1 and adj.p < 0.05 (Figure 2A and Supplementary Figure 1). Besides, 2637 and 2170 lncRNAs related to OS and RFS duration time were screened out by univariate COX regression analysis (p < 0.05). Subsequently, prognostic gene candidates for OS (n = 440) and RFS (n = 351) were determined by overlapping DElncRNAs and univariate COX positive lncRNAs (Figure 2B, 2C). The training cohort and the validation cohort for both OS and RFS classifiers do not have a significant difference. The LASSO regression and multivariate COX analysis were then performed in OS and RFS training group, respectively, at a 20-fold cross-validation manner to generate the lncRNA-based classifiers for OS (Figure 2D, 2E and Supplementary Figure 2A), and RFS (Figure 2F, 2G and Supplementary Figure 2B) prognostics.

Figure 2. Identification of prognostic lncRNAs. (A) Volcano plot showing DElncRNAs identified from the TCGA-LIHC dataset. (B) Venn diagram of prognostic DElncRNAs obtained from crossing DElncRNAs and COX positive lncRNAs in the OS cohort. (C) Venn diagram of prognostic DElncRNAs obtained from crossing DElncRNAs and COX positive lncRNAs in the RFS cohort. (D) LASSO regression in the OS cohort according to Lambda value. (E) The coefficient profiles of prognostic DElncRNAs in the OS cohort. (F) LASSO regression in the RFS cohort according to Lambda value. (G) The coefficient profiles of prognostic DElncRNAs in the RFS cohort.

Construction of OS and RFS prediction classifiers

According to the screening process listed above, an 8-lncRNAs-based classifier for OS prediction and a 6-lncRNAs-based classifier for RFS prediction were constructed. The information of the elemental lncRNAs was listed in detail (Table 1).

Table 1. The detailed information of lncRNAs in OS- and RFS- classifiers.

8 lncRNA-based classifier for OS
Gene ID	Gene name	Chromosome	Start point	End point
ENSG00000230587	LINC02580	2p21	43070403	43143114
ENSG00000234899	SOX9-AS1	17q24.3	72040713	72237203
ENSG00000245248	USP2-AS1	11q23.3	119364359	119527977
ENSG00000246985	SOCS2-AS1	12q22	93503696	93571768
ENSG00000254340	AC022784.5	8p23.1	9137584	9145503
ENSG00000261012	AC115619.1	2p24.1	20999312	21000917
ENSG00000262136	AC092115.3	16q22.1	69726533	69742563
ENSG00000267583	AC007998.3	18q12.2	35435198	35467165
6 lncRNA-based classifier for RFS
Gene ID	Gene name	Chromosome	Start point	End point
ENSG00000223393	AL118511.1	1q42.2	230868259	230879141
ENSG00000254333	NDST1-AS1	5q33.1	150474817	150486291
ENSG00000255571	LINC00925	15q26.1	89361578	89398605
ENSG00000262823	AC127521.1	17p13.2	4480379	4486452
ENSG00000267905	AC008750.2	19q13.41	51340020	51345050
ENSG00000270547	LINC01235	9p23	13404750	13488226

Subsequently, the OS cohort was further divided into two sub-groups (high-risk and low-risk) according to the median value of the risk score calculated by the OS classifier. The distribution of risk scores, the vital status of patients, and expression of element lncRNAs were compared between high-risk and low-risk subgroups of the OS cohort (Figure 3A–3C). The features of the risk score determined by the RFS classifier in the RFS cohort were also shown in a similar manner (Figure 3D–3F).

Figure 3. Division of OS/RFS cohorts into sub-groups by risk score of lncRNA-based classifiers. (A) Distribution of patients the OS whole cohort according to risk score by the classifier. (B) Sub-groups in OS cohorts with different vital status. (C) Expression of lncRNAs from the OS classifier in high- and low-risk groups of the OS cohort. (D) Distribution of patients the RFS whole cohort according to risk score by the classifier. (E) Sub-groups in RFS cohorts with different recurrence status. (F) Expression of lncRNAs from the RFS classifier in high- and low-risk groups of the RFS cohort.

The expression of the lncRNAs from both prognostic classifiers was then compared in the high-risk group, low-risk group, and non-tumor control group, to confirm the differential expression level between the high risk and low risk group. As expected, all lncRNAs for OS prediction (Figure 4A) and RFS prediction (Figure 4B) showed a significant differential expression between the high-risk group and low-risk group, further validating the hypothesis that the expression of these prognostic lncRNAs could be correlated to tumor progression in HCC.

Figure 4. Expression level of lncRNAs from the signatures in different sub-groups. (A) The expression level of lncRNAs consisting OS classifier in control, low-risk and high-risk groups. (B) The expression level of lncRNAs consisting RFS classifier in control, low-risk and high-risk groups.

Assessment of the lncRNA signatures for HCC prognosis prediction

The predictive capacity of both OS- and RFS signatures were evaluated in all the training, validation, and whole cohorts, respectively. Kaplan-Meier log-rank tests were conducted in all 6 groups to confirm the effectiveness and consistency of the model for both OS (Figure 5A–5C) and RFS (Figure 5D–5F) prediction. Unanimously, in all the cohorts with OS and RFS prognostic panels, patients in high-risk groups showed significantly poorer outcomes of either demise or tumor relapse (P < 0.01). These results indicated that the OS- and RFS-classifiers significantly linked with the prognosis of HCC, thus hold the potential as an effective prediction model.

Figure 5. Kaplan–Meier analysis showing the OS- and RFS-time expectancy. (A–C) The overall survival curves of HCC patients in training, validation and whole cohorts grouped by the risk level. (D–F) The relapse-free survival curves of HCC patients in training, validation and whole cohorts grouped by the risk level.

Afterward, the efficiency of both classifiers was checked by the time-dependent receiver operating characteristic (ROC) curve. In the OS cohort, areas under ROC curve (AUCs) of the 8-lncRNA-based classifier reached 0.798, 0.817 and 0.841 for 1, 3, and 5 years in the training group (Figure 6A), 0.729, 0.777 and 0.727 in the validation group (Figure 6B), 0.763, 0.774 and 0.782 for 1, 3, and 5 years in the whole group (Figure 6C). Meanwhile, the AUCs of the 6-lncRNA-based classifier for RFS prediction were 0.845, 0.802, 0.855. for 1, 3, and 5 years in the training group (Figure 6D), 0.688, 0.695, 0.649 in the validation group (Figure 6E), 0.728, 0.733, 0.739 in the whole RFS group (Figure 6F).

Figure 6. The time-dependent ROC curve evaluating the efficiency of lncRNA based classifiers. (A) The ROC curve indicating the efficiency of lncRNA-based classifier as OS prognosis indicator in training, (B) validation, (C) and whole groups. (D) The ROC curve indicating the efficiency of lncRNA-based classifier as RFS prognosis indicator in training, (E) validation, (F) and whole groups.

Comprehensive prognostic analysis of lncRNA signatures and clinical pathological characteristics

As described above, the lncRNA-based signatures were proved to be a prognosis indicator with high accuracy in predicting the outcomes of both OS and RFS time for HCC patients. However, whether the risk score of the novel lncRNA-based classifiers is correlated with other clinicopathologic characteristics requires further exploration.

Hence, some major clinical factors were listed and compared with the risk score with Pearson chi-square test in the OS- and RFS-cohorts, separately (Table 2). The analysis indicated that in the OS cohort, pT and tumor stage were significantly associated with risk score levels. In the RFS cohort, pT and tumor stage also showed relevance with the risk score levels despite less significance. In all, a high-risk score level often implies late pT, higher tumor stage, and after all, short overall survival and relapse-free survival time.

Table 2. Correlations between risk score of the OS- and RFS-classifiers and clinicopathological characteristics.

Clinicopathologic features in the OS cohorts
Variable	High risk	Low risk	Pearson x2	P-value
Age
>60	97	94	0.9914	0.7529
=<60	85	88
Gender
male	122	123	0.01248	0.9110
female	60	59
pT
T3-T4	75	37	18.62	0.0001
T0-T2	107	145
pN
N1-N3	61	56	0.3149	0.5747
N0	121	126
pM
M1	49	53	0.2179	0.6406
M0	133	129
Tumor stage
Stage III-IV	79	36	23.5	0.0001
Stage I-II	103	146
Clinicopathologic features in the RFS cohorts
Variable	High risk	Low risk	Pearson x2	P value
Age
>60	82	78	0.2067	0.6494
=<60	73	77
Gender
male	101	105	0.2315	0.6304
female	54	50
pT
T3-T4	44	28	4.631	0.0314
T0-T2	111	127
pN
N1-N3	44	42	0.06437	0.7997
N0	111	113
pM
M1	32	39	0.8952	0.3441
M0	123	116
Tumor stage
Stage III-IV	50	29	7.491	0.0062
Stage I-II	105	126

To compare the efficiency of lncRNA classifiers and other prognostic factors, age, gender, pT, pN, pM, tumor stage and lncRNA-based classifiers were assessed by a two-step COX regression analysis (Table 3). In OS cohorts, pT, pM, tumor stage and risk score defined by the 8-lncRNA-based classifier were found significantly associated with OS-time in the univariate COX test. Interestingly, only the risk score and pM remained positive as the independent predictor in all the OS groups after multivariate COX analysis, while the risk score revealed a dramatically higher efficiency than pM. Similar in RFS groups, pT, tumor stage, and the 6-lncRNA-based classifier were positively related to RFS-time in univariate COX analysis, yet only the risk score of the RFS lncRNA classifier remained significant in the following multivariate COX with high efficiency.

Table 3. Uni-and multivariate COX regression of the prognostic factors for OS and RFS prediction.

8-lncRNA-based OS classifier
Parameter	Univariate COX	P value	Multivariate COX	P value
	HR (95% CI)		HR (95% CI)
Age (> 60 vs ≤ 60)	1.01(1.00-1.02)	0.175520599
Gender (male vs female)	1.22(0.85-1.75)	0.2775024378
pT (3-4 vs 0-2)	1.65(1.37-2.00)	0.0000001391	1.77(0.95-3.28)	0.070485113
pN (1-3 vs 0)	1.42(0.98-2.05)	0.0644260708
pM (1 vs 0)	1.73(1.19-2.51)	0.0037449858	1.78(1.22-2.58)	0.002747801
Stage (III-IV vs I-II)	1.66(1.36-2.02)	0.0000005323	0.75(0.39-1.43)	0.376683648
Risk score (high vs low)	1.40(1.29-1.53)	0.0000000001	4.01(2.66-6.05)	0.0000000001
6-lncRNA-based RFS classifier
Parameter	Univariate COX	P value	Multivariate COX	P value
	HR (95% CI)		HR (95% CI)
Age (> 60 vs ≤ 60)	1.00(0.98-1.01)	0.6332914949
Gender (male vs female)	0.89(0.62-1.27)	0.5226851602
pT (3-4 vs 0-2)	1.64(1.36-1.97)	0.0000001770	1.77(0.63-4.98)	0.2754281773
pN (1-3 vs 0) pM (1 vs 0)	1.17(0.80-1.69) 1.20(0.82-1.76	0.4162533310 0.3502956657
Stage (III-IV vs I-II)	1.64(1.36-1.99)	0.0000004245	0.80(0.27-2.33)	0.6824451077
Risk score (high vs low)	1.73(1.55-1.93)	0.0000000001	1.45(1.27-1.66)	0.0000000537

As the lncRNA signatures and several pathological features concordantly showed significant correlation with HCC progression, their combined efforts in predicting HCC prognosis were further check with the Normogram analysis. In specific, the risk score is the most relevant indicator in the diagram with the total points reflecting the final prognostic probability in OS of HCC, while the T status also plays a critical role (Figure 7A). In RFS prognosis, despite the risk score of the lncRNA classifier and the T status remained the major anchor for prognosis, factors such as Age and Tumor stage were interestingly gained more weight on deciding total probability points compared with OS prognosis (Figure 7B).

Figure 7. Nomogram including lncRNA-based signature and other pathoclinical factors for both OS and RFS prognosis prediction. (A) Nomogram including risk score determined by the lncRNA-based signature and other pathoclinical factors for OS prognostic assessment of HCC. (B) Nomogram including risk score determined by the lncRNA-based signature and other pathoclinical factors for RFS prognostic assessment of HCC.

Taking together, the lncRNA-based classifiers could be considered as an independent indicator for both OS and RFS prediction of HCC with high efficiency.

Identification of potential small molecules for high-risk score patients

To identify drug candidates for high-risk patients with our LncRNA signature, two different approaches were applied using CTRP and PRISM drug response database separately. The differential drug sensitivity was identified between high- (top 20%) and low-risk (bottom 20%) patients with lower estimated AUC values in high-risk patients (Log2FC >0.05). Following this, the spearman correlation was measured to select the candidates with a negative correlation coefficient (r < -0.2) between risk scores and AUC values with both OS and RFS signatures. With the OS- signature, seven candidates from CTRP and seven candidates from the PRISM dataset were identified (Figure 8A, 8B). And applying the RFS signature, a total of six compounds were screened out from both datasets using the same criteria (Figure 8C, 8D). All candidates were having significantly lower estimated AUC values in high-risk patients. To further investigate the mechanism of these drug candidates, the Cmap mode-of-action (MoA) database including nearly 3000 small-molecule compounds was applied (Figure 8E). The target analysis revealed 14 distinct drug targets in those candidates, and the top enriched targets are HMGCR inhibitors and topoisomerase inhibitors.

Figure 8. Spearman’s correlation analysis and differential drug response analysis of 7 CTRP-derived compounds; (A) and 7 PRISM-derived compounds (B) with OS-classifier; (C) and 3 PRISM-derived compounds; (D) with OS-classifier. Note that lower values on the y-axis of boxplots imply greater drug sensitivity. (E) Heatmap showing each compound (perturbagen) from the CMap dataset that shares mechanisms of action (rows) and sorted by descending number of compound with shared mechanisms of action.

Author Contributions

F.H. and N.W. collected consolidated the data, performed analysis and interpretation of the data. F.H., Y.C, and J.W. designed the study. F.H. wrote the draft of the manuscript. X.W. and J.W. provided help in revising the manuscript.

Acknowledgments

We express our heartfelt thanks to Dr. Chen Qian from Cedars Sinai Medical Center for providing valuable help and instruction on this paper.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This study was supported by Research physician project from Shanghai Jiao Tong University School of medicine (No. 20191901) and Interdisciplinary research foundation for medicine and engineering from Shanghai Jiao Tong University (No. YG2021QN21).

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