Identification of Ten-LncRNA Signatures Predict Disease-Free and Overall Survival for Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-613111/v1

Background: The prognosis of hepatocellular carcinoma (HCC) is bleak though it has been improved over recent years. Early diagnosis could improve the survival. Plenty of researches indicate that long non-coding RNAs (lncRNAs) could play an important role in prognostic prediction of cancer as a kind of biomarker.

Methods: We downloaded clinicopathological characteristics and lncRNA expression data of HCC patients from The Cancer Genome Atlas (TCGA) database. The ratio of training sets to validation sets was 2:1. Significant differentially expressed lncRNAs were identified by log-rank test and cox regression. All the significant lncRNAs were selected into the least absolute shrinkage and selection operator regression (LASSO) analysis and constructed risk-score formula by linear combination. Performance of the signatures were validated by receiver operating characteristics (ROC) curves and Kaplan-Meier survival curves. The correlated messenger RNAs (mRNA) were evaluated by functional enrichment analysis.

Results: We identified and validated ten-lncRNAs based signatures to predict disease-free survival (DFS) and overall survival (OS) of HCC respectively. Stratified survival analysis showed that the performance of lncRNAs related signatures was better than tumor, node, metastasis(TNM) staging system. Functional enrichment analysis showed that organelle fission and regulation of mRNA metabolic process were significantly enriched in differentially expressed lncRNAs (DElncRNAs). Transcriptional misregulation in cancer and mitogen-activated protein kinase (MAPK) signaling pathway were significantly enriched pathways in the pathway enrichment analysis.

Conclusion: we constructed two lncRNAs based signatures which could predict prognosis of HCC more accurate than the traditional ways.

Cancer Biology

Molecular Genetics

Oncology

long non-coding RNA

prognosis

risk score

hepatocellular carcinoma

HCC is a aggressive malignancy with high incidence and mortality(1). Due to the high rates of metastasis and recurrence, the overall survival rate of HCC remains low though great efforts have been made in the diagnosis and treatments(2, 3). Consequently, it is urgent to identify reliable molecular biomarkers that can predict early recurrence and prognosis of HCC to help physicians with decision-making.

Long non-coding RNAs are transcripts longer than 200 nucleotides that have no protein-coding potential, however, they play a vital role in lots of physiologic processes(4–6). Over the last few decades, genome-wide sequencing facilitated the discovery of a series of lncRNAs. Recently, increasing evidences have indicated abnormal expression of lncRNAs is frequent in various cancers, and is closely linked to cancer initiation, development, progression, and metastasis(7–10). Emerging studies suggested that lncRNAs might act as potential predictors for clinical outcomes in cancers, including HCC(11–14).

Currently, with the advancements of the big data era, high-throughput data and clinical data could be achieved by mining previously published public databases such as The Cancer Genome Atlas and The Gene Expression Omnibus (GEO). Researches have developed a couple of systems biology methods to model lncRNA-based signatures in order to predict prognosis more accurately(15–18). But in fact, the lncRNA signatures that can effectively predict the recurrence and prognosis of HCC as well as their prognostic value have not yet been fully explored.

In this study, we screened prognosis-associated lncRNAs and established a prognostic risk assessment model through analyzing lncRNA expression records of 371 HCC patients from TCGA database. Two ten-lncRNA signatures for disease-free survival and overall survival respectively were constructed and successfully validated. It turns out these lncRNA—based models could be served as dependable prognostic predictors for HCC.

Data acquisition

The lncRNA expression data and clinicopathological characteristics of HCC patients were downloaded from the TCGA database (https://xenabrowser.net/datapages), including 371 HCC tissues and 50 adjacent non-tumor tissues. 371 patients with available survival information were enrolled in the study totally. Clinicopathological parameters of the HCC patients are shown in Table 1. The ratio of training sets to validation sets was 2:1.

Data processing

DElncRNAs were screened by edgeR and limma packages from the entire lncRNA expression profiling data between tumor and normal samples. The threshold was set at |log2 fold change (FC)|>1.5 and p adj < 0.05. Meanwhile, univariate Cox regression was conducted to screen out lncRNAs that were significantly associated with DFS and OS using the survival package in R. The significance of the identified lncRNAs was tested by log-rank test, and p < 0.01 was considered statistically significant.

Construction of lncRNAs signature

The significant lncRNAs were selected into the LASSO(19) regression analysis at 20-fold cross-validation to get the correlation coefficients. By linearly combining the expression level of selected lncRNAs weighted by the estimated coefficients, the following risk-score formula was constructed for calculation: Risk Score = exp_gene1 * β_gene1 + exp_gene2 * β_gene2 +^…+ exp_genen* β_genen (exp, expression level, β, the regression coefficient in the LASSO regression analysis). According to the cut-off of the median risk score, HCC patients were classified into high- and low-risk group.

Confirmation of lncRNAs signature

Comparison of the differences of DFS and OS between high-risk and low-risk group was performed by Kaplan-Meier survival curves and log-rank tests in all sets. Differentiation of clinicopathological characteristics between high-risk group and low-risk group was evaluated by Pearson’s χ² test. Univariate and multivariate Cox regression analyses were adopted to screen out the independent prognostic factors in the lncRNA-related signature and other clinical parameters. ROC were plotted to assess the specificity and sensitivity of lncRNA classifier in HCC prognostic prediction. All the analyses were performed by R software with the following packages: ‘edgeR’, ‘glmnet’, ‘survival’ and ‘survminer’. A P value less than 0.05 was considered statistically significant.

Functional enrichment analyses

To screen target genes of DElncRNAs, human lncRNA-mRNA interaction was download from redundant arrays of independent disk (RAID) 2.0 database (www.rna-society.org/raid/). The correlated mRNAs were evaluated by functional enrichment analysis to explore the functions of the prognostic DElncRNAs using the clusterProfiler. Significantly enriched gene ontology (GO) terms and kyoto encyclopedia of genes and genomes (KEGG) pathways with P value < 0.05 were visualized using the R software.

Identification of prognosis-associated DElncRNAs

The workflow of the study is displayed in Fig. 1. As shown in Fig. 2A, B, a total of 232 DElncRNAs with |logFC| >1.5 and padj < 0.05 were identified by R package ‘limma’. Of them, 191 lncRNAs were found to be upregulated and 41 to be downregulated, which indicated that these DElncRNAs could distinguish HCC from normal tissues. In addition, to determine significant prognosis-associated lncRNAs (p < 0.01) in HCC, univariate Cox regression was performed. In total, 2113 lncRNAs were chosen for further analyses. Among them, 1193 and 920 lncRNAs were significantly correlated with DFS and OS, respectively. Following this, 60 DFS-related and 45 OS-related DElncRNAs were selected for the next step of the analysis (Fig. 2C, D).

Construction of lncRNAs signature

LASSO regression was adopted to build the risk score model for prognosis prediction in the training cohort at a 20-fold cross-validation (Fig. 3). 10 lncRNAs related to DFS and 10 lncRNAs related to OS were selected by lambda.min value. The expression levels of these prognostic lncRNAs and their coefficients were constructed signatures respectively. The disease free survival risk score = (0.13915 * AC009005.2) + (0.14837 * CASC9) + (0.26053 * CTC_338M12.5) + (0.13069 * DYNLL1_AS1) + (-0.06954 * LINC01018) + (0.08498 * PRRT3_AS1) + (0.13913 * RP11_488L18.10) + (0.22265 * RP11_739N20.2) + (0.19098 * RP11_977G19.5) + (0.06799 * WAC_AS1). The overall survival risk score = (0.10825 * AC007405.6) + (-0.12949 * CTC_297N7.9) + (0.17571 * CTD_2510F5.4) + (-0.07341 * F11_AS1) + (-0.00409 * LINC00152) + (0.31658 * LINC01138) + (0.20993 * PCAT6) + (0.21144 * PRRT3_AS1) + (0.25757 * RP11_307C12.11) + (0.31922 * RP11_479G22.8).

Validation and confirmation of prognostic performance of the lncRNAs signature

Using those formulas to figure up the risk score of each patient. All patients were classified into high-risk and low-risk groups in the light of the cut off value. Kaplan Meier curves showed patients with high-risk scores had shorter DFS (P < 0.0001) and OS (P < 0.0001) than those with low-risk scores in the training group. Similar results were observed in the validation and entire set (Fig. 4).

In addition, we analyzed the clinicopathological characteristics between the high-risk and low-risk group in training, validation and entire set respectively by chi-square test. Detailed information was showed in Table 2. For the DFS-related group, the distribution of grade between high-risk and low-risk group showed a significant difference in the training set(P = 0.002). In the validation set, there were statistically significant differences in pT(P = 0.008), stage(P = 0.006) and grade(P = 0.004) between high-risk and low-risk group. For the OS-related group, similar results were obtained in the entire set. There were significant differences in pT(P = 0.002), stage(P = 0.004) and grade(P < 0.001) between high-risk and low-risk group. The proportion of patients with pT3-pT4, stage III-IV and grade 3–4 were higher in the high-risk group than low-risk group. On the other hand, for the DFS-related cohort and OS-related cohort, patients with pT3-pT4, stage III-IV and grade 3–4 had higher risk scores than the patients with pT1-pT2, stage I-II and grade 1–2 in the entire set (Fig. 5).

Univariate and multivariate Cox regression were adopted with the lncRNA-based signature and clinicopathological characteristics. The outcome was presented in Fig. 6. The lncRNA-based signature was an independent predictive factor of DFS and OS for training and entire sets (P < 0.05). In the univariate analyses, pT and stage of the tumor were demonstrated to be risk factors for both DFS and OS of hepatocellular carcinoma patients in training, validation and entire cohorts (P < 0.05). Pathological metastasis was also a significant risk factor for DFS and OS in the univariate regression (P < 0.05).

The performance of the 10-lncRNA signature was evaluated by the area under the ROC curve. The area under curve (AUC) value of the 1,3,5-year DFS predicted in the entire set was 0.73, 0.69 and 0.68 respectively (Fig. 7A). The AUC value of the 1,3,5-year OS predicted in the entire set was 0.76, 0.72 and 0.71 (Fig. 7B). All of the AUC values showed the good performance of lncRNA-based signature. To explore the prognostic value of lnc-RNA based signature, we compared the AUC value of lncRNA-based signature and American joint committee on cancer (AJCC) 8th TNM stage (Fig. 8). The AUC value of TNM stage for DFS was 0.67 lower than the AUC value of lncRNA-based signature. However, the AUC value of TNM stage combined lncRNA-based signature was 0.79 better than the AUC value of TNM stage or lncRNA-based signature alone. Similar results were obtained for OS analysis, which suggested that the combination of TNM stage and lncRNA-based signature could improve the prognosis predictive ability.

What’ more, in order to further analyze the applicable clinical characteristics of lncRNA-based signature, we divided all the patients into different subgroups by TNM stage integrating with lncRNA-based signature. Kaplan Meier curves were performed in the subgroups (Fig. 9). The survival curves indicated that patients of stage I/II in the low-risk group had better survival rate than which in the high-risk group (P < 0.0001), patients of stage III/IV in the high-risk group had worse survival prognosis than which in the low-risk group (P < 0.0001).

Functional enrichment analyses

Firstly, 3212 and 250 target genes that were co-expressed with DFS/OS related lncRNAs were extracted from RAID 2.0 database. These significantly correlated genes were used for GO analysis and KEGG enrichment analysis to determine the potential mechanism of the DFS/OS related lncRNAs in regulating HCC (Fig. 10). GO enrichment analysis found the DFS related DElncRNAs were mainly enriched in organelle fission, nuclear division mitotic nuclear division, and chromosome segregation, etc. Moreover, regulation of mRNA metabolic process, regulation of RNA stability and pri-miRNA transcription by RNApolymeraseII were the most enriched function with OS related DElncRNAs. KEGG analysis showed that the target gene of DFS related DElncRNAs had significant link to lots of enriched pathways, including cell cycle, p53 signaling pathway, tumor necrosis factor signaling pathway, and MAPK signaling pathway. What’s more, the KEGG analysis of OS related DElncRNAs revealed that their targeted genes were involved in transcriptional misregulation in cancer, human T-cell leukemia virus 1 infection, interleukin-17 signaling pathway, cell cycle, and transforming growth factor–beta signaling pathway.

HCC has high clinical and genic heterogeneity with high recurrent rate. Effective prognostic biomarkers are useful for physicians to take appropriate treatments to extend the disease free survival and overall survival. Recently several researches discovered non-coding RNA acted on carcinogenesis and tumor progression, including proliferation, metastasis, metabolic regulation and drug resistance, etc(20–25). Lots of dysregulated lncRNAs show great potential as biomarker in prognostic evaluation of cancer. Several lncRNA-based prognostic models have been used for many malignancies, such as carcinoma of lung, bladder and breast(26–30), etc. Several researches have constructed lncRNA-based risk models of HCC, but the existing risk models mostly based on the overall survival rather than disease free survival(31–34). In this study, we screened both DFS-associated and OS-associated DElncRNAs in the TCGA databases by univariate Cox and LASSO regression, then established two lncRNA-based signatures focusing on DFS and OS respectively, both of the prognostic models were validated and showed good performance.

Several studies suggested that the expression level of LINC 00152 was increased in HCC and reported to be a great diagnostic lncRNA with good performance(35, 36). LINC 00152 was found enhancing migration and invasion of tumor cells by neuro trophin receptor kinase (NTRK)3 and promoting proliferation of tumor by activating the mammalian target of rapamycin (mTOR) pathway (37, 38). Except LINC 00152, rest of lncRNAs were the first time to be reported as prognostic factors of HCC as far as we knew. GO and KEGG enrichment analyses were conducted to figure out molecular mechanism of DElncRNAs related with DFS/OS. GO enrichment analysis showed that organelle fission and regulation of mRNA metabolic process were the most enriched functions of DFS and OS respectively. KEGG analysis suggested that the most enriched pathways of DFS and OS were MAPK signaling pathway and transcriptional misregulation in cancer respectively. These results indicated that these lncRNAs may involve in carcinogenesis and tumor development by those biological functions and pathways. However, the detailed mechanism still needs further experiments to demonstrate.

TNM stage is the most commonly used prognostic evaluation method during the clinical practice. We contrasted the performance of lncRNA-related model and TNM stage by AUC value and Kaplan Meier curve, demonstrating that lncRNA-based signatures had better performance in the prognostic prediction. Precise stratified analysis can help physicians make appropriate clinical decisions and is beneficial to improve prognosis at some extent. Recently, several studies suggested that patients received liver transplantation had better overall survival and lower recurrence rate than which treated with hepatectomy in early HCC(39–41). Thus, it’s crucial to select applicable candidates because of the shortage of donors. In the Kaplan Meier analysis, the survival of patients in stage I/II with low-risk were better than which with the high-risk (P < 0.0001), which may be helpful with identifying the appropriate candidates of liver transplantation. However, patients of stage III/IV with high-risk had worse survival than which with low-risk(P < 0.0001), which may need more aggressive treatment strategies and more closely follow-up.

Our study have several limitations. Primarily, entire molecular mechanism of these lncRNAs in HCC development remains unknown, which needs further experiments to figure out. Secondly, part of momentous clinicopathological features associated with prognosis were not included in this study because of absence in the TCGA dataset, combining with clinicopathological features may improve the accuracy of the signatures. Finally, this is a retrospective research from TCGA database, the performance of these lncRNA-based signatures need to be validated by a larger scale external cohort.

We built two 10-lncRNA related prognostic model for DFS and OS respectively by univariate Cox and LASSO regression which stratified HCC patients more precise than TNM staging system and could be useful tools to provide accurate prognostic information for clinical physician with decision-making. However, there remains the needs of larger-scale studies to validate the prognostic accuracy of the signatures and the potential molecular functions of these lncRNAs.

HCC – Hepatocellular carcinoma

LncRNAs – long non-coding RNAs

TCGA database – The Cancer Genome Atlas database

LASSO regression – least absolute shrinkage and selection operator regression

ROC curve – receiver operating characteristics curve

mRNA – messenger RNA

DFS – disease-free survival

OS – overall survival

TNM staging system – tumor, node, metastasis staging system

DElncRNAs – differentially expressed lncRNAs

MAPK signaling pathway – mitogen-activated protein kinase

GEO database – The Gene Expression Omnibus database

RAID – redundant arrays of independent disk

GO – gene ontology

KEGG – Kyoto encyclopedia of genes and genomes

AUC – area under curve

AJCC – American joint committee on cancer

NTRK – neuro trophin receptor kinase

mTOR – mammalian target of rapamycin

Ethics approval and consent to participate

The study was carried out in accordance with relevant guidelines and regulations and approved by the ethics committee of west china hospital, Sichuan university.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analyzed during the study are available in the http://xenabrowser.net/datapages and www.rna-society.org/raid/.

Competing interests

The authors declare that they have no competing interests.

Funding

None.

Authors' contributions

SS Yang and J Lu analyzed the data and wrote the manuscript; XZ Xiong designed and supervised the writing of the manuscript; all authors approved the final manuscript as submitted.

Acknowledgements

None.

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Table 1. Baseline data of HCC patients.

Characteristic	Classification	n (%)
Total		371
Age, years	>60	193 (52.0)
	≤60	177 (47.7)
	Not available	1 (0.3)
pT	T1	181 (48.8)
	T2	94 (25.3)
	T3	80 (21.5)
	T4	13 (3.5)
	Tx	1 (0.3)
	Not available	2 (0.6)
pN	N0	252 (67.9)
	N1	4 (1.1)
	Nx	114 (30.7)
	Not available	1 (0.3)
pM	M0	266 (71.7)
	M1	4 (1.1)
	Mx	101 (27.2)
Stage	Stage I	171 (46.1)
	Stage II	86 (23.2)
	Stage III	85 (22.9)
	Stage IV	5 (1.3)
	Not available	24 (6.5)
Grade	G1	55 (14.8)
	G2	177 (47.7)
	G3	122 (32.9)
	G4	12 (3.2)
	Not available	5 (1.3)

Table 2. The clinical characteristics between the high-risk and low-risk group divided by the DFS-related (A) and OS-related (B) lncRNAs signature.

A.

Variable	Training set			Validation set			Entire set
Variable	Number High/low	x-squared	p-value	Number High/low	x-squared	p-value	Number High/low	x-squared	p-value
Age		0.006	0.936		2.750	0.097		0.082	0.775
≤60	44/58			22/31			81/80
>60	46/62			30/22			75/79
pT		0.598	0.439		7.008	0.008		4.379	0.036
T1-2	65/90			35/47			100/137
T3-4	25/27			17/6			42/33
pN		0.057	0.811		1.943	0.163		1.508	0.219
N0	62/87			33/33			95/120
N1	1/1			2/0			3/1
pM		1.233	0.267		0.003	0.954		0.462	0.497
M0	70/87			38/35			108/122
M1	1/0			1/1			2/1
Stage		1.412	0.235		7.477	0.006		6.259	0.012
I-II	60/85			31/44			91/129
III-IV	26/25			17/6			43/31
Grade		9.463	0.002		8.353	0.004		17.708	<0.001
G1-2	47/86			25/40			72/126
G3-4	42/31			27/13			69/44

B.

Variable	Training set			Validation set			Entire set
Variable	Number High/low	x-squared	p-value	Number High/low	x-squared	p-value	Number High/low	x-squared	p-value
Age		0.067	0.796		0.447	0.504		0.346	0.556
≤60	53/64			28/28			81/92
>60	55/71			29/37			84/108
pT		7.326	0.007		2.279	0.131		9.279	0.002
T1-2	68/104			43/56			111/160
T3-4	40/28			14/9			54/37
pN		0.045	0.832		-	-		0.044	0.834
N0	71/88			40/49			111/137
N1	2/2			0/0			2/2
pM		0.856	0.355		1.794	0.180		2.610	0.106
M0	80/93			43/47			123/140
M1	0/1			0/2			0/3
Stage		6.195	0.013		2.565	0.109		8.166	0.004
I-II	62/98			42/52			104/150
III-IV	37/28			14/8			51/36
Grade		5.168	0.023		17.591	<0.001		17.800	<0.001
G1-2	57/90			28/55			85/145
G3-4	49/42			29/10			78/52

Identification of Ten-LncRNA Signatures Predict Disease-Free and Overall Survival for Hepatocellular Carcinoma

Status:

Version 1

Abstract

Figures

Background

Methods

Data acquisition

Data processing

Construction of lncRNAs signature

Confirmation of lncRNAs signature

Functional enrichment analyses

Results

Identification of prognosis-associated DElncRNAs

Construction of lncRNAs signature

Validation and confirmation of prognostic performance of the lncRNAs signature

Functional enrichment analyses

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Status:

Version 1