Comprehensive analysis of differentially expressed miRNAs in hepatocellular carcinoma: Prognostic, predictive significance and pathway insights

Kayleigh Smith; Dan Beach; Roger Silva; Gyorffy Balazs; Francesca Salani; Francesco Crea

doi:10.1371/journal.pone.0296198

. 2024 Apr 18;19(4):e0296198. doi: 10.1371/journal.pone.0296198

Comprehensive analysis of differentially expressed miRNAs in hepatocellular carcinoma: Prognostic, predictive significance and pathway insights

Kayleigh Smith ^1,^‡, Dan Beach ^2,^‡, Roger Silva ³, Gyorffy Balazs ^4,⁷, Francesca Salani ^1,^5,^6,^‡,^*, Francesco Crea ^1,^‡

Editor: Junming Yue⁸

PMCID: PMC11025735 PMID: 38635644

Abstract

Robust prognostic and predictive factors for hepatocellular carcinoma, a leading cause of cancer-related deaths worldwide, have not yet been identified. Previous studies have identified potential HCC determinants such as genetic mutations, epigenetic alterations, and pathway dysregulation. However, the clinical significance of these molecular alterations remains elusive. MicroRNAs are major regulators of protein expression. MiRNA functions are frequently altered in cancer. In this study, we aimed to explore the prognostic value of differentially expressed miRNAs in HCC, to elucidate their associated pathways and their impact on treatment response. To this aim, bioinformatics techniques and clinical dataset analyses were employed to identify differentially expressed miRNAs in HCC compared to normal hepatic tissue. We validated known associations and identified a novel miRNA signature with potential prognostic significance. Our comprehensive analysis identified new miRNA-targeted pathways and showed that some of these protein coding genes predict HCC patients’ response to the tyrosine kinase inhibitor sorafenib.

1. Introduction

Primary liver cancer is the second most common cause of cancer mortality in the world [1]. Hepatocellular carcinoma (HCC) is the most common histology identified in primary liver cancers. HCC prognosis is primarily determined by stage, liver function, patient conditions and, indirectly, by HCC aetiology [2]. The five-year survival ranges from more than 50% in operable diseases to less than 5% in the most advanced stages [3]. This is due to the lack of effective treatments, especially in the setting of high tumour burden, where systemic treatments can lead to a median survival of less than two years. Despite extensive molecular studies which have identified potential determinants among genetic mutations, epigenetic alterations and pathway dysregulation [4], HCC pathogenesis is not entirely elucidated. A better understanding of the molecular processes driving HCC could lead to more effective treatments.

MicroRNAs (miRNAs) are short non-coding RNAs that regulate mRNA translation and stability [5]. The human genome encodes approximately 2000 miRNAs, but each of these transcripts can regulate the expression of hundreds of mRNAs. It has been estimated that miRNAs regulate the expression of approximately 60% of the protein-coding genes. The expression of miRNAs is deregulated in many cancers [6], making them attractive biomarkers and therapeutic targets. The role of specific miRNAs in HCC has been studied [7], but there are only limited experiences on comprehensive studies assessing the prognostic function of miRNAs in HCC and the downstream pathways in this malignancy [8–10] leading to the identification of non-univocal signatures.

In this study, we used a combination of bioinformatic techniques and clinical dataset analyses to identify miRNAs that are differentially expressed in HCC versus normal hepatic tissue and whose expression is associated with HCC prognosis. This approach allowed us to confirm known associations, identify several uncharacterised miRNAs and propose new potential targeting pathways with potential clinical relevance.

2. Material and methods

2.1 Differential expression analysis

A data-led approach was chosen over a literature-based candidate gene-identifying approach for the sake of analysis comprehensiveness. OncoMir Cancer Database (OMCD) [11] was used to access data on miRNA expression from Liver hepatocellular carcinoma (referred to as LIHC) dataset. LIHC dataset comprises The Cancer Genome Atlas (TCGA) data from 426 samples, of which 375 HCC patients and 51 non-HCC liver tissue. Differential expression between HCC and non-HCC liver tissue was tested via t-test on OMCD portal (Bonferroni-adjusted p values of < 0.05) and miRNAs whose expression in HCC was at least 2 folds different (ratios > 2 or < 0.5) from non-HCC were retained for further analyses.

2.2 Survival analysis

The shortlisted differentially expressed miRNAs were cross-referenced with miRNAs RNA-seq TCGA dataset provided by Kaplan-Meier Plotter (KMP) tool [12]. Only RNA-seq data from TCGA samples of patients who had not undergone any other treatment (either radio- or chemotherapy or systemic drugs) than surgery was comprised in the survival analysis. Patients were dichotomized according to each differentially expressed miRNA median values, into high- and low-expression cohorts. Overall survival (OS) was chosen as the prognostic endpoint of interest and compared among high- and low-expression patients through log-rank test p-value. Each comparison is presented as median OS (mOS), Hazard Ratio (HR) with 95% Interval of Confidence (95%IC) and p-value.

2.3 Construction of a prognostic miRNA signature

MiRNAs identified in 2.2 bearing a significant prognostic value were used to generate a prognostic signature through miRpower tool in the KMP. “Multiple genes” and “mean gene expression” options were set; patients were dichotomized according to the median value of the signature and the OS of the two cohorts was compared via log-rank test.

2.4 Pathway analysis

Pathway analysis was conducted to explore the mechanisms underpinning the prognostic effects of these differentially expressed miRNAs. This involved the identification of target genes for up- and down-regulated miRNAs, and KEGG pathway analysis using the Over Representation Analysis (ORA) method.

2.4.1 Selection of mature miRNAs

The TCGA miRNA expression data used by the OMCD and KM Plotter databases do not differentiate between mature 3p and 5p miRNAs [13]. Both miRNA forms are capable of binding to target mRNAs through a sequence-specific base pairing mechanism and regulate gene expression by interacting with distinct sets of target genes. However, only one mature strand is typically incorporated into the RNA-induced silencing complex (RISC) and involved in regulating mRNA transcription. Thus, for each miRNA, it was necessary to determine whether the 3p or 5p form should be used for the analysis of downstream target genes and pathways. The predominant mature form of each miRNA was selected based on the number of experimental reads in the miRBase database [14] (S1 Table). The hsa-miR-3653 identifier was subsequently excluded as the available experimental data no longer supports its annotation as a miRNA [14].

2.4.2 Target gene identification

The first step in conducting the pathway analysis was to choose a database that provides miRNA-target interaction data. MirTarBase is a manually curated database of experimentally validated miRNA-target interactions (MTI) collated from reporter assay, western blot and next generation sequencing (NGS) studies [15]. The database includes more than 360,000 MTIs and represents the largest and most up to date source of experimentally validated MTI data. MirTarBase was initially selected due to its size and rigorous inclusion criteria. An alternative MTI database, miRPathDB was found to include the latest version mirTarBase data and was selected instead. The prognostic mature miRNA identifiers were queried in the mirRPathDB database, retrieving a comprehensive list of target genes. This list was cross-validated using the miRTargetLink 2.0 database which provided a second mirror of the most recent mirTarBase data, confirming a 100% match between the predictions provided by the two databases.

A Python script was written to remove duplicates and genes with no experimental evidence of miRNA-interaction. Two genes could not be mapped to current Ensembl/Entrez IDs and were excluded from further analysis. The remaining target genes were relevant and aligned with the objective of the analysis.

2.4.3 Functional annotation and enrichment analysis

Functional annotation and enrichment analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Alternative online functional enrichment tools WebGestalt and g:Profiler were evaluated, but DAVID was selected because it is highly cited, regularly updated, and well documented [16]. The target gene lists for up-regulated miRNAs and down-regulated miRNAs were uploaded to DAVID which identified them as being for the Homo Sapiens species. Functional annotation enrichment was performed for targets of upregulated miRNAs and downregulated miRNAs separately, selecting only the KEGG pathway database option. Statistical analysis tested the hypothesis that the overlap between genes in the target gene list and a given pathway or functional category was greater than expected by chance. The statistical test used was Fisher’s exact test and Benjamini-Hochberg correction was applied to p-values to correct for multiple tests. Text-based result files for each gene list were downloaded from DAVID. A Python script was written to filter pathways with a Benjamini-Hochberg significance threshold of ≤ 0.05.

2.4.4 Analysis of enriched KEGG pathways

To focus on the most relevant pathways, KEGG analysis results were filtered to include only pathways that are putatively involved in HCC. Relevant pathways were defined as those listed as “related” in the KEGG HCC Pathway (hsa05225) [17]. A Python script was used to filter the KEGG results according to a predefined set of keywords derived from these maps. The keyword list used to filter results was: Hepatocellular, Hepatitis, Alcohol, NAFLD, PI3K, AKT, P53, WNT, MAPK, Cell cycle, calcium signaling, TGF-beta.

2.4.5 Validation of the functional enrichment analysis

A validation of the DAVID functional enrichment analysis was performed using the WebGeSTALT tool, accessed in February 2024 [18].

2.4.6 Identifying potential miRNA interactions with cancer driver genes

A previously reported pan-cancer analysis of more than 9000 tumor exomes from 33 TCGA projects identified 299 oncogenic driver genes and 3473 associated driver missense mutations [19]. Subsequent functional validation utilizing an independent dataset of experimentally validated mutations indicated that 60–85% of the predicted mutations were likely to be cancer drivers. A list of 299 HCC and pan-cancer associated genes was retrieved from TCGA and a Python script was used to return intersection between the prognostic miRNA target genes and the 299 putative cancer driver genes.

3. Results

3.1 Some differentially expressed miRNAs are prognostic in resected HCC

Using OMCD, levels of expression of miRNAs in HCC versus healthy liver tissue were compared. Out of the 106 significantly differentially expressed miRNAs, 59 had a significant, higher than 2-fold differential expression (HCC/healthy liver either >2 or <0.5), compared to normal liver: respectively, 23 were increased and 36 decreased in HCC (S2 Table). Forty of the differentially expressed miRNAs were also present in the KMP RNA-seq dataset which we have employed for prognostic stratification (21 of which up-regulated and 19 down-regulated in HCC versus healthy liver).

Of the 40 shortlisted miRNAs, 10 showed a statistically significant correlation with OS (p for log-rank test <0.05); in particular, 5 among the HCC up-regulated (hsa-miR-501, hsa-miR-877, hsa-miR-1180, hsa-miR-3127, hsa-miR-3677) and 5 among the HCC down-regulated miRNAs (hsa-miR-3653, hsa-miR-99a, hsa-miR-145, hsa-miR-326, hsa-miR-139) (Table 1).

Table 1. OS correlation of differentially expressed miRNAs (> 2-fold increase or decrease in HCC vs normal livers), according to KMP; miRNAs in bold are significantly correlated with OS.

MiRNA identifier	P value	HR	Median survival—Low expression (Months)	Median survival -High expression (Months)
Increased expression in HCC
hsa-miR-660	0.6338	1.09 (0.77–1.54)	58.88	55.4
hsa-miR-581	0.4568	1.14 (0.81–1.61)	55.69	53.33
hsa-miR-93	0.455	1.14 (0.81–1.62)	55.69	80.75
hsa-miR-10b	0.2934	1.21 (0.85–1.71)	55.4	58.88
hsa-miR-532	0.2351	1.24 (0.87–1.75)	55.4	83.24
hsa-miR-34a	0.2247	0.81 (0.57–1.14)	55.4	58.88
hsa-miR-183	0.1846	1.27 (0.89–1.79)	70.06	45.93
hsa-miR-500b	0.1732	1.27 (0.9–1.81)	70.06	45.57
hsa-miR-96	0.1296	1.31 (0.92–1.85)	70.06	45.93
hsa-miR-224	0.1179	1.32 (0.93–1.88)	70.06	46.78
hsa-miR-222	0.0882	1.35 (0.95–1.92)	70.06	51.29
hsa-miR-589	0.0865	1.36 (0.96–1.92)	60.89	51.29
hsa-miR-221	0.0696	1.38 (0.97–1.96)	70.06	51.29
hsa-miR-500a	0.0666	1.38 (0.98–1.96)	69.57	58.88
hsa-miR-452	0.0657	1.39 (0.98–1.97)	80.75	45.93
hsa-miR-21	0.0552	1.41 (0.99–2)	70.06	46.78
hsa-miR-501	0.05	1.42 (1–2.01)	69.57	55.4
hsa-miR-877	0.0158	1.54 (1.08–2.18)	80.75	45.57
hsa-miR-1180	0.013	1.56 (1.1–2.23)	69.57	45.93
hsa-miR-3127	0.0055	1.65 (1.15–2.35)	83.57	45.93
hsa-miR-3677	0.0000015	2.42 (1.67–3.51)	80.75	37.32
Decreased expression in HCC
hsa-miR-542	0.8858	0.97 (0.69–1.38)	60.89	55.69
hsa-miR-214	0.8781	1.03 (0.73–1.45)	70.06	53.33
hsa-miR-29c	0.8026	0.96 (0.68–1.35)	81.73	55.4
hsa-miR-199b	0.7972	1.05 (0.74–1.48)	60.89	55.4
hsa-miR-33b	0.5578	1.11 (0.78–1.57)	69.57	53.33
hsa-miR-3614	0.5502	0.9 (0.64–1.27)	60.89	55.69
hsa-miR-10a	0.4714	0.88 (0.62–1.25)	70.06	55.4
hsa-miR-497	0.4714	0.88 (0.62–1.25)	60.89	55.4
hsa-miR-26b	0.4599	1.14 (0.8–1.62)	107.11	48.99
hsa-miR-424	0.3057	1.2 (0.85–1.7)	81.73	51.29
hsa-let-7c	0.166	0.78 (0.55–1.11)	40.41	69.57
hsa-miR-195	0.1263	0.76 (0.54–1.08)	53.39	58.88
hsa-miR-130a	0.1166	0.76 (0.53–1.07)	51.29	69.57
hsa-miR-3607	0.1029	0.75 (0.53–1.06)	45.57	70.06
hsa-miR-3653	0.0376	0.69 (0.49–0.98)	41.79	80.75
hsa-miR-99a	0.0333	0.68 (0.48–0.97)	41.79	70.06
hsa-miR-145	0.0326	0.68 (0.48–0.97)	46.78	69.57
hsa-miR-326	0.0002	1.97 (1.37–2.83)	81.73	45.57
hsa-miR-139	0.00000057	0.41 (0.28–0.59)	30.61	80.75

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Abbreviations: miRNA, microRNA; HCC, hepatocellular carcinoma; HR, hazard ratio; OS, overall survival.

In keeping with our hypothesis, we found that for the majority of miRNAs, expression in HCC versus normal tissue and prognostic significance were highly correlated. All the miRNAs up-regulated in HCC were associated with negative prognosis, 4 out of 5 miRNAs that are down-regulated in HCC were associated with better prognosis. Hsa-miR-326 was the only transcript decreased in HCC showing an unexpected negative prognostic role (HR:1.97(1.37–2.83), p 0.0002).

To increase the robustness of our analysis, a 9-miRNA prognostic signature was generated from prognostically relevant miRNAs (this signature excluded Hsa-miR-326, which showed inconsistent correlations between prognosis and expression patterns in normal vs neoplastic liver tissues). The 9-miRNA signature yielded an HR for OS of 2.15 (1.52–3.05), with log-rank p of 0.00005 (Fig 1A), consistent across different clinical sub-groups, as shown in Fig 1B–1F.

Fig 1 — Nine-miRNA prognostic signature predicting overall survival in the overall HCC cohort (A) and sub-groups: White-Caucasians (B), Asians (C), well-differentiated (D) and poorly-differentiated HCCs (E), high-TMB HCCs (F).

The prognostic significance in terms of OS of each miRNA belonging to the signature is plotted separately in S2 Fig.

Taken together, these results suggest that the 5 miRNAs up-regulated in HCC and associated with worse prognosis may be oncogenic, whilst the 4 miRNAs that are down-regulated in HCC and associated with better prognosis may act as tumour suppressors. Based on these criteria, we could not predict a mechanistic role for Hsa-miR-326. For this reason, we decided to also study the functional pathways associated with this peculiar transcript.

3.2 Functional pathways of miRNA-targeted genes

To understand which molecular pathways are modulated by differential miRNA expression in HCC, we used a dataset of validated miRNA/mRNA target pairs. This enabled us to identify potential functional differences between the two types of miRNAs. A total of 982 unique target protein-coding genes for the 10 miRNAs were retrieved from MiRPathDB. Among these, 430 genes were targeted by up-regulated miRNAs, while 585 genes were targeted by down-regulated ones. We then ran pathway analyses on the two separate lists of protein coding genes.

Pathway analysis on target genes was conducted with DAVID and Webgestalt. Target genes of up-regulated miRNAs were significantly enriched in 7 (DAVID) and 1 (Webgestalt) KEGG pathways. The associated pathways were mainly implicated in neurodegenerative diseases. After filtering for HCC-specific pathways, cell cycle was the only pathway associated with targets of up-regulated miRNAs in both DAVID and Webgestalt tools (Table 2).

Table 2. HCC-specific pathways relative to up-regulated miRNAs.

Term (KEEG)	Genes	%	P-Value	Fold Enrichment	Benjamini	FDR
hsa04110:Cell cycle	12	2.790698	0.000356	3.716564	0.019756	0.019392

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Abbreviations: FDR, false discovery rate.

Targets of down-regulated miRNAs were significantly enriched in 93 (DAVID) and 94 (Webgestalt) KEGG pathways relating to numerous cancers, cancer related processes and associated signalling pathways. Ten HCC-specific pathways were significantly enriched (p<0.05), including HCC, Hepatitis B, MAPK signalling pathway, PI3K-Akt signalling pathway, Cell cycle, Hepatitis C, TGF-beta signalling and p53 signalling pathway (Table 3).

Table 3. HCC-specific pathways relative to down-regulated miRNAs.

Term (KEGG)	Genes	%	P-Value	Fold Enrichment	Benjamini	FDR
hsa05200:Pathways in cancer	58	9.7643	3.41E-12	2.71604428	2.11E-10	1.44E-10
hsa05225:Hepatocellular carcinoma	30	5.0505	1.73E-11	4.44033101	7.66E-10	5.22E-10
hsa05161:Hepatitis B	25	4.2088	2.39E-08	3.83732310	3.91E-07	2.66E-07
hsa04010:MAPK signaling pathway	34	5.7239	6.48E-08	2.87564294	8.74E-07	5.95E-07
hsa04151:PI3K-Akt signaling pathway	36	6.0606	5.86E-07	2.52873088	6.27E-06	4.27E-06
hsa05206:MicroRNAs in cancer	32	5.3872	2.11E-06	2.56679780	2.05E-05	1.39E-05
hsa04110:Cell cycle	16	2.6936	0.000146	3.15756872	8.54E-04	5.81E-04
hsa05160:Hepatitis C	18	3.0303	0.000174	2.85086220	9.65E-04	6.57E-04
hsa04350:TGF-beta signaling pathway	12	2.0202	0.001279	3.17436430	5.43E-03	3.70E-03
hsa04115:p53 signaling pathway	9	1.5152	0.008519	3.06565319	3.01E-02	2.05E-02

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Abbreviations: FDR, false discovery rate.

A graphical representation of the results of the above-mentioned enrichment analyses is presented in Fig 2.

A Comprehensive overview of KEGG HCC-specific pathways can be found in S1 Fig.

The process of intersecting the miRNA target genes with the HCC and pan cancer associated genes revealed that the list of 982 miRNA target genes contained 10 HCC driver genes and 11 pan-cancer driver genes (Table 4).

Table 4. HCC- and pan-cancer driver genes among miRNA target genes.

Symbol	Ensembl Gene ID	Chr	Targeting miRNAs	Position (Mbp)	Description
THRAP3	ENSG00000054118	1	hsa-miR-877-5p, hsa-miR-3127-5p, hsa-miR-326, hsa-miR-145-5p, hsa-miR-99a-5p	36.2244	thyroid hormone receptor associated protein 3
NRAS (^*)	ENSG00000213281	1	hsa-mir-501-3p, hsa-miR-877-5p, hsa-miR-139-5p, hsa-miR-145-5p, hsa-miR-326	114.7045	NRAS proto-oncogene, GTPase
NOTCH2	ENSG00000134250	1	hsa-miR-139-5p, hsa-miR-326, hsa-miR-3127-5p, hsa-miR-3677-3p, hsa-miR-145-5p	119.9116	notch receptor 2
TBL1XR1	ENSG00000177565	3	hsa-miR-139-5p, hsa-mir-501-3p, hsa-miR-326, hsa-miR-145-5p, hsa-miR-3677-3p, hsa-miR-877-5p, hsa-miR-3127-5p	177.0193	TBL1X receptor 1
PIK3CA ( ^* )	ENSG00000121879	3	hsa-mir-501-3p, hsa-miR-326, hsa-miR-139-5p, hsa-miR-145-5p, hsa-miR-877-5p	179.1481	phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha
MSH3	ENSG00000113318	5	hsa-mir-501-3p, hsa-miR-139-5p, hsa-miR-877-5p, hsa-miR-326, hsa-miR-145-5p	80.6547	mutS homolog 3
H1-4 ( ^* )	ENSG00000168298	6	hsa-miR-1180-3p	26.1563	H1.4 linker histone, cluster member
CDKN1A ( ^* )	ENSG00000124762	6	hsa-miR-3127-5p, hsa-miR-877-5p, hsa-miR-145-5p	36.6765	cyclin dependent kinase inhibitor 1A
EEF1A1 ( ^* )	ENSG00000156508	6	hsa-miR-877-5p	73.4893	eukaryotic translation elongation factor 1 alpha 1
ESR1	ENSG00000091831	6	hsa-mir-501-3p, hsa-miR-3127-5p, hsa-miR-99a-5p, hsa-miR-877-5p, hsa-miR-3677-3p, hsa-miR-145-5p, hsa-miR-326, hsa-miR-139-5p	151.6567	estrogen receptor 1
MYC	ENSG00000136997	8	hsa-miR-145-5p	127.7354	MYC proto-oncogene, bHLH transcription factor
KMT2A	ENSG00000118058	11	hsa-miR-139-5p, hsa-miR-877-5p, hsa-miR-1180-3p, hsa-miR-3127-5p, hsa-miR-326	118.4365	lysine methyltransferase 2A
KRAS ()*	ENSG00000133703	12	hsa-miR-877-5p, hsa-mir-501-3p, hsa-miR-3127-5p, hsa-miR-1180-3p, hsa-miR-145-5p, hsa-miR-326	25.2052	KRAS proto-oncogene, GTPase
RB1 ( ^* )	ENSG00000139687	13	hsa-miR-3127-5p, hsa-miR-99a-5p, hsa-miR-326	48.3037	RB transcriptional corepressor 1
CHD8	ENSG00000100888	14	hsa-miR-877-5p	21.3852	chromodomain helicase DNA binding protein 8
BRD7 ( ^* )	ENSG00000166164	16	hsa-miR-3127-5p, hsa-miR-145-5p, hsa-mir-501-3p, hsa-miR-326, hsa-miR-3677-3p, hsa-miR-99a-5p	50.3135	bromodomain containing 7
BCL2	ENSG00000171791	18	hsa-miR-145-5p, hsa-miR-3127-5p, hsa-miR-99a-5p, hsa-miR-139-5p, hsa-mir-501-3p	63.1233	BCL2 apoptosis regulator
SMARCA4 ( ^* )	ENSG00000127616	19	hsa-miR-139-5p	10.9609	SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4
RPS6KA3 ( ^*)	ENSG00000177189	X	hsa-miR-139-5p, hsa-miR-145-5p, hsa-miR-326, hsa-miR-877-5p	20.1499	ribosomal protein S6 kinase A3
USP9X	ENSG00000124486	X	hsa-miR-1180-3p, hsa-miR-877-5p, hsa-mir-501-3p, hsa-miR-145-5p, hsa-miR-139-5p	41.0854	ubiquitin specific peptidase 9 X-linked
AR	ENSG00000169083	X	hsa-miR-139-5p, hsa-miR-99a-5p, hsa-miR-877-5p, hsa-miR-145-5p, hsa-miR-326, hsa-miR-3127-5p	67.544	androgen receptor

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(*) refer to HCC-specific genes.

To further validate the clinical significance of our findings, we studied the predictive value of HCC-specific miRNA-targeted protein coding genes (Table 4) in a small but well characterised cohort of HCC patients treated with sorafenib. Notably, higher expression of 3 miRNA-targeted genes (RB1, NOTCH2, PIK3CA) was associated with better survival (Fig 3A–3C). In each case, higher expression of the protein coding genes was a positive predictive factor (longer survival in sorafenib-treated patients). The three protein coding genes are targeted by 3 to 5 of our investigated miRNAs (Table 4, fourth column). Notably, the only miRNA that targets all these three protein-coding genes is Hsa-miR-326, which is down regulated in HCC, but whose higher expression is associated with worse prognosis. Moreover, the combined three-gene signature achieved an even greater predictive significance (Fig 3D).

Fig 3 — (A) *RB1;* (B) *NOTCH2*; (C) *PIK3CA*. (D) Kaplan Meier plot for the 3-genes signature in the same population.

Thus, there is experimental evidence that the investigated miRNAs may also directly interact with 21 putative HCC and pan-cancer driver genes.

4. Discussion

In this study, we have identified a 9-miRNA prognostic signature for HCC. The proposed signature is consistently significant across different clinically relevant HCC sub-groups (e.g. ethnicity, nuclear differentiation grade) and seems to have a prominent prognostic role among Asian patients, possibly due to more frequent HBV-aetiology in this ethnic group [20]. In keeping with this hypothesis, HBV-aetiology is the second most significant HCC-pathway affected by our miRNA signature (Fig 2). If confirmed in clinical datasets, this signature could enable patient stratification and treatment optimisation, especially for low- and intermediate-risk diseases. At these stages, several therapeutic options are available, but currently these treatments cannot be tailored based on individual molecular profiles. Interestingly, some miRNAs are well detectable in biological fluids, such as plasma [21]. If the 9-miRNA signature proves to be detectable in plasma samples in the HCC population, it can be used as a minimally invasive biomarker for patient stratification and dynamic monitoring.

Several experimental studies have dissected the pathogenic role of specific miRNAs in HCC. Our results are in line with mechanistical evidence. Of the five up-regulated/negatively prognostic miRNAs, four have been shown to act as oncogenes in HCC cells: mir-501 by promoting epithelial to mesenchymal transition [22]; mir-3127 and mir-1180 by promoting proliferation and tumorigenesis [23, 24]; mir-3677 by promoting proliferation and drug resistance [25]. While the clinical significance of some of these transcripts has been described, to the best of our knowledge this is the first study showing that mir-3127 has a prognostic impact on HCC. Notably, our pathway analysis has identified cell cycle regulation as the main molecular pathway controlled by these miRNAs, further confirming the link between bioinformatic and experimental findings.

Our results on mir-877 are apparently in contrast with previous reports, showing that this transcript inhibits HCC proliferation [26]. We found that this miRNA is up-regulated in HCC versus normal tissue, and that higher expression of this miRNA predicts poor prognosis. Both results imply an oncogenic function. Due to these coherent results, we decided to maintain mir-877 in our prognostic signature. Notably, another study showed that a genetic variant of this miRNA predicts HCC risk [27]. It is not known if this genetic variant has a prognostic role in advanced disease settings. It is therefore conceivable that this miRNA could play different roles at different stages of disease progression (e.g. onco-suppressive for early stage, oncogenic for advanced stage), as demonstrated for other cancer-related genes [28]. In light of the partially conflicting evidence, further studies are warranted to dissect the role of this miRNA at different stages of HCC progression, and to test its prognostic role in other clinical datasets.

In parallel with the results on putative oncogenic miRNAs, our results on putative tumour suppressors agree with pre-clinical results. Mir-3653, mir-326 and mir-139 have been shown to inhibit metastasis and progression of HCC cells [29–31]. Similarly, mir-145 has been implicated in G2/M phase arrest by inhibiting cyclin B1 [32]. Finally, a recent study has shown that mir-99a is a direct target of the epigenetic silencer EZH2 [33]. EZH2 promotes cell proliferation, metastasis and drug resistance in several cancers by silencing the expression of tumour suppressors [34]. We have shown that higher EZH2 activity (measured via a liquid biopsy approach) predicts poorer prognosis in HCC patients treated with sorafenib [35]. Hence our finding on mir-99a paves the way for the combined use of miRNA and epigenetic biomarkers for patient stratification and drug efficacy prediction in HCC.

Other recent manuscripts have investigated miRNA signatures in HCC. For example, Sathipati et al identify a 23-miRNA signature associated with stage [10]. Notably, 7 of these miRNAs had a prognostic role, based on the analysis of a dataset of 166 HCC samples. We acknowledge that some aspects of the methodology overlap with our study. However, there are also substantial differences between the two studies. Sathipati et al have used machine learning for miRNA selection, focussing on transcript expression at different cancer stages, whilst we have manually filtered the transcripts based on differential expression in HCC vs normal tissue and we have subsequently refined our signature based on prognostic value. Importantly, we have validated our prognostic signature in a larger dataset containing data from 372 HCC samples. Our 9-miRNA signature does not contain any of the 7 miRNAs identified by Sathipati et al. This divergence is attributable to the substantially different methodologies of the two studies. Interestingly 3 out of 5 HCC-specific upregulated miRNAs have been measured in biological fluids from cancer patients [36–38]. This result suggests the potential clinical relevance of our miRNA signature to guide clinical diagnosis and prognosis.

We also investigated the predictive role of miRNA-modulated pathways in a curated cohort of HCC patients exposed to the tyrosine kinase inhibitor sorafenib. Sorafenib is employed for the treatment of advanced HCC as second line treatment after progression on first-line immunotherapy-based strategies, or as front-line choice for patients with absolute contraindications to combination strategies. Our results showed that higher expression of three target genes was associated with better response to the treatment. Notably, the only miRNA that targets all these three protein-coding genes is mir-326, which is down regulated in HCC, but whose higher expression is associated with worse prognosis. Because of this intriguing inconsistence, we decided to perform pathway analyses on this transcript. Based on our results, it is conceivable that this miRNA could play different roles at different stages of HCC pathogenesis. Whilst inhibiting early tumorigenic events, mir-326 could suppress the expression of therapy-sensitivity genes (NOTCH, RB1, PIK3CA) thereby conferring sorafenib resistance to HCC cells. These findings highlight the potential predictive role of the miRNA signature and of the associated target mRNAs.

In conclusion, we have identified for the first time a miRNA signature that is differentially expressed in HCC and significantly predicts prognosis. This signature includes transcripts whose function has been independently validated in pre-clinical studies. Future studies on clinical samples (primary tumours and plasma) are warranted to confirm the clinical usefulness of this potential new biomarker panel.

Supporting information

S1 Table. Number of experimental reads of the predominant mature form of each miRNA.

(DOCX)

pone.0296198.s001.docx^{(5.9KB, docx)}

S2 Table. Fifty-nine differentially expressed miRNAs (2-fold) in HCC vs normal livers according to OMCD.

(XLSX)

pone.0296198.s002.xlsx^{(8.3KB, xlsx)}

S1 Fig. KEGG HCC pathways.

Enriched genes from target up- or down-regulated miRNAs are red-highlighted.

(TIF)

pone.0296198.s003.tif^{(198.4KB, tif)}

S2 Fig. Kaplan Meier plots showing prognostic significance for OS of each miRNA of the signature.

(TIF)

pone.0296198.s004.tif^{(210.2KB, tif)}

Data Availability

Data are publicly available from the repositories referenced in the manuscript.

Funding Statement

The author(s) received no specific funding for this work.

References

1.McGlynn KA, Petrick JL, El-Serag HB. Epidemiology of Hepatocellular Carcinoma. Hepatology. 2021. Jan;73(Suppl 1):4–13. doi: 10.1002/hep.31288 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Ito T, Nguyen MH. Perspectives on the Underlying Etiology of HCC and Its Effects on Treatment Outcomes. Journal of Hepatocellular Carcinoma. 2023;10:413. doi: 10.2147/JHC.S347959 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Reig M, Forner A, Rimola J, Ferrer-Fàbrega J, Burrel M, Garcia-Criado Á, et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. Journal of Hepatology. 2022. Mar 1;76(3):681–93. doi: 10.1016/j.jhep.2021.11.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Tümen D, Heumann P, Gülow K, Demirci CN, Cosma LS, Müller M, et al. Pathogenesis and Current Treatment Strategies of Hepatocellular Carcinoma. Biomedicines. 2022. Dec;10(12):3202. doi: 10.3390/biomedicines10123202 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Naeli P, Winter T, Hackett AP, Alboushi L, Jafarnejad SM. The intricate balance between microRNA-induced mRNA decay and translational repression. The FEBS Journal. 2023;290(10):2508–24. doi: 10.1111/febs.16422 [DOI] [PubMed] [Google Scholar]
6.Smolarz B, Durczyński A, Romanowicz H, Szyłło K, Hogendorf P. miRNAs in Cancer (Review of Literature). Int J Mol Sci. 2022. Mar 3;23(5):2805. doi: 10.3390/ijms23052805 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Li J, Bao H, Huang Z, Liang Z, Wang M, Lin N, et al. Little things with significant impact: miRNAs in hepatocellular carcinoma. Frontiers in Oncology [Internet]. 2023. [cited 2023 Nov 29];13. Available from: https://www.frontiersin.org/articles/10.3389/fonc.2023.1191070 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Chen Z, Zhang L, Ding C, Ren K, Wan D, Lin S. A six-miRNA signature as a novel biomarker for improving prediction of prognosis and patterns of immune infiltration in hepatocellular carcinoma. Am J Transl Res. 2022. Jun 15;14(6):3610–37. [PMC free article] [PubMed] [Google Scholar]
9.A five‐miRNA expression signature predicts survival in hepatocellular carcinoma—Liu—2017—APMIS—Wiley Online Library [Internet]. [cited 2023 Nov 29]. Available from: https://onlinelibrary.wiley.com/doi/10.1111/apm.12697 [DOI] [PubMed]
10.Yerukala Sathipati S., Ho SY. Novel miRNA signature for predicting the stage of hepatocellular carcinoma. Sci Rep 10, 14452 (2020). doi: 10.1038/s41598-020-71324-z [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Sarver AL, Sarver AE, Yuan C, Subramanian S. OMCD: OncomiR Cancer Database. BMC Cancer. 2018. Dec 6;18(1):1223. doi: 10.1186/s12885-018-5085-z [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Discovery Győrffy B. and ranking of the most robust prognostic biomarkers in serous ovarian cancer. GeroScience. 2023. Mar 1;45(3):1889–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Kuo WT, Su MW, Lee YL, Chen CH, Wu CW, Fang WL, et al. Bioinformatic Interrogation of 5p-arm and 3p-arm Specific miRNA Expression Using TCGA Datasets. J Clin Med. 2015. Sep 15;4(9):1798–814. doi: 10.3390/jcm4091798 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019. Jan 8;47(Database issue):D155–62. doi: 10.1093/nar/gky1141 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Huang HY, Lin YCD, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions. Nucleic Acids Res. 2021. Nov 30;50(D1):D222–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Research. 2022. Jul 5;50(W1):W216–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Research. 2021. Jan 8;49(D1):D545–51. doi: 10.1093/nar/gkaa970 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Research. 2019. Jul 2;47(W1):W199–205. doi: 10.1093/nar/gkz401 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018. Apr 5;173(2):371–385.e18. doi: 10.1016/j.cell.2018.02.060 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Lin J, Zhang H, Yu H, Bi X, Zhang W, et al. Epidemiological Characteristics of Primary Liver Cancer in Mainland China From 2003 to 2020: A Representative Multicenter Study. Front Oncol. 2022. Jun 21;12:906778. doi: 10.3389/fonc.2022.906778 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Watahiki A, Macfarlane RJ, Gleave ME, Crea F, Wang Y, Helgason CD, et al. Plasma miRNAs as Biomarkers to Identify Patients with Castration-Resistant Metastatic Prostate Cancer. International Journal of Molecular Sciences. 2013. Apr;14(4):7757–70. doi: 10.3390/ijms14047757 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Yu W, Deng W, Zhao Q, Zhuang H, Zhang C, Jian Z. miR-501 acts as an independent prognostic factor that promotes the epithelial–mesenchymal transition through targeting JDP2 in hepatocellular carcinoma. Human Cell. 2019. Jul 1;32(3):343–51. doi: 10.1007/s13577-019-00243-7 [DOI] [PubMed] [Google Scholar]
23.Jiang J, Zhang Y, Guo Y, Yu C, Chen M, Li Z, et al. MicroRNA-3127 promotes cell proliferation and tumorigenicity in hepatocellular carcinoma by disrupting of PI3K/AKT negative regulation. Oncotarget. 2015. Jan 31;6(8):6359–72. doi: 10.18632/oncotarget.3438 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Zhou X, Zhu H qiang, qun Ma C, Li H guang, Liu F feng, Chang H, et al. MiR-1180 promoted the proliferation of hepatocellular carcinoma cells by repressing TNIP2 expression. Biomedicine & Pharmacotherapy. 2016. Apr 1;79:315–20. doi: 10.1016/j.biopha.2016.02.025 [DOI] [PubMed] [Google Scholar]
25.He H, Zhou J, Cheng F, Li H, Quan Y. MiR-3677-3p promotes development and sorafenib resistance of hepatitis B-related hepatocellular carcinoma by inhibiting FOXM1 ubiquitination. Human Cell. 2023. Sep 1;36(5):1773–89. doi: 10.1007/s13577-023-00945-z [DOI] [PubMed] [Google Scholar]
26.Huang X, Qin J, Lu S. Up-regulation of miR-877 induced by paclitaxel inhibits hepatocellular carcinoma cell proliferation though targeting FOXM1. Int J Clin Exp Pathol. 2015. Feb 1;8(2):1515–24. [PMC free article] [PubMed] [Google Scholar]
27.Wang H, Wang B, Wang T, Fan R. A genetic variant in the promoter region of miR-877 is associated with an increased risk of hepatocellular carcinoma. Clinics and Research in Hepatology and Gastroenterology. 2020. Oct 1;44(5):692–8. doi: 10.1016/j.clinre.2020.01.006 [DOI] [PubMed] [Google Scholar]
28.Qin Y, Ashrafizadeh M, Mongiardini V, Grimaldi B, Crea F, Rietdorf K, et al. Autophagy and cancer drug resistance in dialogue: Pre-clinical and clinical evidence. Cancer Letters. 2023. Aug 28;570:216307. doi: 10.1016/j.canlet.2023.216307 [DOI] [PubMed] [Google Scholar]
29.Zhang L, Zhang T, Deng Z, Sun L. MicroRNA-3653 inhibits the growth and metastasis of hepatocellular carcinoma by inhibiting ITGB1. Oncology Reports. 2019. Mar 1;41(3):1669–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Wong CC, Wong C, Tung EK, Au SL, Lee JM, Poon RT, et al. The MicroRNA miR-139 Suppresses Metastasis and Progression of Hepatocellular Carcinoma by Down-regulating Rho-Kinase 2. Gastroenterology. 2011. Jan 1;140(1):322–31. doi: 10.1053/j.gastro.2010.10.006 [DOI] [PubMed] [Google Scholar]
31.Liu C, Xu K, Liu J, He C, Liu P, Fu Q, et al. LncRNA RP11-620J15.3 promotes HCC cell proliferation and metastasis by targeting miR-326/GPI to enhance glycolysis. Biol Direct. 2023. Apr 5;18:15. doi: 10.1186/s13062-023-00370-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Lai Y, Liu J, Hu X, Zeng X, Gao P. Modifications of The Human Liver Cancer Cells through microRNA-145-Mediated Targeting of CDCA3. Cell J. 2023. Aug;25(8):546–53. doi: 10.22074/cellj.2023.1995666.1251 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Wu CS, Chien YC, Yen C, Wu JY, Bai LY, Yu YL. EZH2-mediated epigenetic silencing of tumor-suppressive let-7c/miR-99a cluster by hepatitis B virus X antigen enhances hepatocellular carcinoma progression and metastasis. Cancer Cell International. 2023. Sep 9;23(1):199. doi: 10.1186/s12935-023-03002-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Crea F, Fornaro L, Bocci G, Sun L, Farrar WL, Falcone A, et al. EZH2 inhibition: targeting the crossroad of tumor invasion and angiogenesis. Cancer Metastasis Rev. 2012. Dec 1;31(3):753–61. doi: 10.1007/s10555-012-9387-3 [DOI] [PubMed] [Google Scholar]
35.Salani F, Latarani M, Casadei-Gardini A, Gangadharannambiar P, Fornaro L, Vivaldi C, et al. Predictive significance of circulating histones in hepatocellular carcinoma patients treated with sorafenib. Epigenomics. 2022. May;14(9):507–17. doi: 10.2217/epi-2021-0383 [DOI] [PubMed] [Google Scholar]
36.Juracek J, Madrzyk M, Trachtova K, Ruckova M, Bohosova J, et al. Combination of Urinary MiR-501 and MiR-335 With Current Clinical Diagnostic Parameters as Potential Predictive Factors of Prostate Biopsy Outcome. Cancer Genomics Proteomics. 2023. May-Jun;20(3):308–316. doi: 10.21873/cgp.20383 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Guo Y, Zhang X, Wang L, Li M, Shen M, et al. The plasma exosomal miR-1180-3p serves as a novel potential diagnostic marker for cutaneous melanoma. Cancer Cell Int. 2021. Sep 20;21(1):487. doi: 10.1186/s12935-021-02164-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Sun Y, Chen C, Zhang P, Xie H, Hou L, et al. Reduced miR-3127-5p expression promotes NSCLC proliferation/invasion and contributes to dasatinib sensitivity via the c-Abl/Ras/ERK pathway. Sci Rep. 2014. Oct 6;4:6527. doi: 10.1038/srep06527 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0296198.r001

Decision Letter 0

Junming Yue

3 Jan 2024

PONE-D-23-40355Comprehensive analysis of differentially expressed miRNAs in Hepatocellular carcinoma: prognostic significance and pathway insightsPLOS ONE

Dear Dr. Salani,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Authors need to address following concerns:1. There is a similar study on miRNA profile in HCC. please address how this study is different from published one. In particular, need to address those novel miRNAs identified in this study.2. It is not clear how important those miRNAs identified and correlated with clinical signatures including overall or progression free survival. Is there any evidence to show they are potential biomarkers for diagnosis or prognosis.

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Reviewer #2: Partly

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Reviewer #1: In this study, the authors studied the miRNAs in hepatocellular carcinoma to identify a set of miRNA that can potentially be used for HCC prognosis prediction. While some of these miRNAs have already been reported in literature to be associated with HCC, the authors were generally transparent about this and provided clear comparisons between their results versus the reported ones. Overall I found this manuscript of potential interest to the readers of this journal and could provide some value to researchers in this field. However, the following comments need to be addressed:

1. In the title there is no need to capitalize the first letter of "Hepatocellular"

2. What does "co-last authors" mean? I thought it means co-corresponding, but only one of them is labeled as the corresponding author, so I am a bit confused.

3. line 31, the authors claimed they identified "novel miRNAs" with is misleading since most of their reported miRNAs have already been shown in literature to be associated with HCC. Please revise this sentence to be more objective.

4. line 53 "bioinformatic" is a single world

5. line 108 please specify how many genes have been removed in this step, and how many are left to proceed with next steps.

6. Section 2.4.5 needs more details. Please specify how many pathways were identified to be enriched by both methods, and how many are not. Did the authors proceed with only the ones enriched in both methods?

7. line 232-240, it is interesting that the authors found the role of miR-877 to be contradicting with some literature. The authors provided a plausible hypothesis that miR-877 could be playing opposite roles in different stages of HCC. However, following this hypothesis, should we still consider miR-877 as a prognostic biomarker for HCC? The authors should comment on this in their discussion based on their own study and analysis.

8. line 250-251, I think it is too assertive to claim that these 9-miRNA signature "robustly predicts prognosis" since this study solely relies on pubic database with no additional experimental validation.

Reviewer #2: The manuscript by K Smith analyzed miRNA data of HCC patients from public database (TCGA) and identified a 9-miRNA signature for HCC patient’s prognosis. The authors also did some pathway analysis and compared their findings with the published HCC and pan cancer associated genes. Here are some concerns:

1. Novelty. This manuscript is very similar to an article published in 2020 (DOI: https://doi.org/10.1038/s41598-020-71324-z), which identified a 23-miRNA signature for HCC stage and prognosis using machine learning. The authors did not discuss this publication in this manuscript.

2. The authors compared the miRNA target genes with published cancer associated gene set, and then concluded that there are ‘direct miRNA interactions with cancer driver genes’, which is not sound enough for the conclusion.

3. Figure 1B and 1C show big differences between White-Caucasians and Asians patients. It would be more interesting and meaningful if the authors discussed the possible reasons.

4. It is worth to show the survival rate of patients related with each miRNA in the 9 miRNAs.

**********

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Reviewer #2: No

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PLoS One. 2024 Apr 18;19(4):e0296198. doi: 10.1371/journal.pone.0296198.r002

Author response to Decision Letter 0

22 Feb 2024

All the information are given in the rebuttal letter uploaded. Please find the same information cut and pasted here as well.

We thank the Editor for having considered our work for publication in PlosOne journal, and we would like to thank the Reviewers for their valuable suggestions.

Herein we provide a specific rebuttal for each point raised by the two Reviewers and the general comments of the Editor.

Editor’s comments

1. There is a similar study on miRNA profile in HCC. please address how this study is different from published one. In particular, need to address those novel miRNAs identified in this study.

We have now extensively discussed the study and the substantial novelty of our findings. Please see response “I” (to Reviewer 2) and associated amendments to the manuscript.

2. It is not clear how important those miRNAs identified and correlated with clinical signatures including overall or progression free survival. Is there any evidence to show they are potential biomarkers for diagnosis or prognosis.

Our study has identified miRNAs that are up-regulated in HCC and prognostically relevant. These two aspects are crucial for diagnosis and prognosis. To highlight the clinical relevance of this signature, we have now discussed the feasibility of detecting these transcripts in biological fluids (see lines 285-288 and references 35-37).

To further increase the clinical relevance of our findings, we have investigated whether any of the predicted mRNA target genes is associated with response to sorafenib in HCC patients (Figure 3). As discussed, sorafenib is currently used in the treatment of this malignancy.

Unfortunately, we could not study the predictive role of miRNAs in sorafenib-treated patients as the dataset only contained protein-coding genes. Nonetheless, we think that these two amendments will increase the clinical relevance of our study.

Reviewer 1:

A) In the title there is no need to capitalize the first letter of "Hepatocellular"

Edited as suggested.

B) What does "co-last authors" mean? I thought it means co-corresponding, but only one of them is labelled as the corresponding author, so I am a bit confused.

During the process of submission, we had asked the Editor if it would have been possible to identify two co-last Authors (similarly to the two co-primary ones), since Crea and Salani had contributed equally to study conception, student supervision, data interpretation and critical revision. This possibility is granted by some international journals, but we had not received a clear reply from the Editor in this regard. If not allowed by PlosOne, we will edit according to the Editor’s recommendation.

C) line 31, the authors claimed they identified "novel miRNAs" which is misleading since most of their reported miRNAs have already been shown in literature to be associated with HCC. Please revise this sentence to be more objective.

We acknowledge that each of the 10 miRNAs has been already identified as HCC-related, mainly in pre-clinical studies. We discuss previously published evidence in lines 248-275, whilst noting that the prognostic role of some transcripts is novel (see for example new added sentence in line 252-254). In addition, we would like to point out that these transcripts have never been comprehensively included in a single prognostic HCC signature (https://doi.org/10.1093/carcin/bgad062; https://doi.org/10.1371/journal.pone.0128628;https://doi.org/10.1002/hep.22160).

Considering the Reviewer’s suggestion, we rephrased “novel miRNAs” with “a novel miRNA signature”.

D) line 53 "bioinformatic" is a single world

Edited as suggested.

E) line 108 please specify how many genes have been removed in this step, and how many are left to proceed with next steps.

There was a 100% match between target genes in the mirPathDB and mirTargetLink database, so it was not necessary to remove any genes at this stage. We have now specified this in Methods (current line 109).

F). Section 2.4.5 needs more details. Please specify how many pathways were identified to be enriched by both methods, and how many are not. Did the authors proceed with only the ones enriched in both methods?

We thank the Reviewer for this important question, which allowed us to clarify our methodology. In the Results section (lines 138, 187-191, 194) we now specify how many pathways were present in both datasets and which ones were overlapping.

G) Line 232-240, it is interesting that the authors found the role of miR-877 to be contradicting with some literature. The authors provided a plausible hypothesis that miR-877 could be playing opposite roles in different stages of HCC. However, following this hypothesis, should we still consider miR-877 as a prognostic biomarker for HCC? The authors should comment on this in their discussion based on their own study and analysis.

We thank the Reviewer for the point raised. As requested, we have explained why we have maintained this miRNA in our prognostic signature: we found internally coherent results in our datasets (up-regulation in HCC vs normal; negative prognostic value. See line 257-266). We acknowledge that further studies are needed to elucidate miR-877 function in HCC and to “test” (not to “confirm” as previously stated) its prognostic role in independent datasets.

H) Line 250-251, I think it is too assertive to claim that these 9-miRNA signature "robustly predicts prognosis" since this study solely relies on public database with no additional experimental validation.

We thank the Reviewer for the suggestion. We amended the sentence to “significantly predict prognosis”.

Reviewer 2:

The manuscript by K Smith analyzed miRNA data of HCC patients from public database (TCGA) and identified a 9-miRNA signature for HCC patient’s prognosis. The authors also did some pathway analysis and compared their findings with the published HCC and pan cancer associated genes. Here are some concerns:

I) Novelty. This manuscript is very similar to an article published in 2020 (DOI: https://doi.org/10.1038/), which identified a 23-miRNA signature for HCC stage and prognosis using machine learning. The authors did not discuss this publication in this manuscript.

We thank the Reviewer for highlighting this interesting study, which is now cited in the Introduction (line 53) and extensively discussed in lines 276-288.

To further increase the novelty of our findings, we have explored whether the protein-coding genes targeted by our prognostic miRNAs were associated with sorafenib response (Figure 3). This correlation had been never investigated in similar studies. This analysis was enabled by the availability of a small but well curated dataset that contained only mRNA genes. Our results are interesting and provide potential explanations on the prognostic role of mir-326 (see lines 295-300).

J) The authors compared the miRNA target genes with published cancer associated gene set, and then concluded that there are ‘direct miRNA interactions with cancer driver genes’, which is not sound enough for the conclusion.

We apologies for the over-statement, and we agree with the Reviewer that we only performed a bioinformatic prediction. To reflect this, we have changed the title of section 2.4.6 which is now:” Identifying potential miRNA interactions with cancer driver genes”.

K) Figure 1B and 1C show big differences between White-Caucasians and Asians patients. It would be more interesting and meaningful if the authors discussed the possible reasons.

The substantial overall survival difference between Asian and Caucasian patients is probably due to divergent HCC aetiology as highlighted by many epidemiological studies: HBV-related (Asians) vs HCV or non-viral aetiology (Caucasians). See this study for reference https://doi.org/10.1111/liv.15251, 10.4254/wjh.v7.i12.1708. We now discuss this aspect in lines 237-241.

L) It is worth to show the survival rate of patients related with each miRNA in the 9 miRNAs.

Kaplan Meier plots of overall survival associated with the 9 different miRNAs have been added to the supplementary material, as Figure S2 (lines 173-174).

PLoS One. doi: 10.1371/journal.pone.0296198.r003

Decision Letter 1

Junming Yue

6 Mar 2024

PONE-D-23-40355R1Comprehensive analysis of differentially expressed miRNAs in hepatocellular carcinoma: prognostic, predictive significance and pathway insightsPLOS ONE

Dear Dr. Salani,

Please submit your revised manuscript by Apr 20 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Junming Yue

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:==============================Please reconcile miR-326 with 10 miRNA signature throughout the manuscript and have to be consistent including in discussion.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #2: One concern now is that the authors stated they found a 9--miRNA signature for HCC based on their hypothesis, miRNA-326 was removed because of its unexpected correlation with prognosis. However, the following study of pathways and miRNA-target genes all included miRNA-326 (line 184), which makes it very confusing. It also says 10-miRNA signature in the discussion (line 285), which needs to be clarified. The pathways and target genes of niRNA-326 could be studied and discussed separately since it has opposite correlation with prognosis than expected.

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Reviewer #2: No

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PLoS One. 2024 Apr 18;19(4):e0296198. doi: 10.1371/journal.pone.0296198.r004

Author response to Decision Letter 1

18 Mar 2024

We would like to thank the Editor and Reviewer2 for pointing out the inconsistency in the miRNA-signature definition. We have now carefully reviewed the manuscript, making sure that the phrase “prognostic signature” is applied only to the group of 9 (not 10) miRNAs. We hope this will reduce confusion.

In this study, we have also tried to investigate potential molecular pathways associated with notable miRNAs. For this mechanistical analysis, we decided to keep the “odd” miRNA that showed opposite correlation between prognosis and expression patterns in normal vs neoplastic liver tissue (mir-326). In the new version of the manuscript, we have made this distinction clear (lines 179-188). As suggested by the reviewer, this miRNA has been discussed separately (lines 300-305).

In the process of this further manuscript’s revision, we also edited:

- A missing reference in line 242 (added between bracket)

- A typo in line 295

- . instead of , in line 32-33

- Patients’ instead of patients, in line 34

- Pathway instead of pathways, in line 45

- “The Cancer Genome Atlas (TCGA)” was misspelled through out the text and not it has been reconciled.

- Ran instead of run, in line 189

- And removed from line 290

PLoS One. doi: 10.1371/journal.pone.0296198.r005

Decision Letter 2

Junming Yue

20 Mar 2024

Comprehensive analysis of differentially expressed miRNAs in hepatocellular carcinoma: prognostic, predictive significance and pathway insights

PONE-D-23-40355R2

Dear Dr. Salani,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Junming Yue

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0296198.r006

Acceptance letter

Junming Yue

27 Mar 2024

PONE-D-23-40355R2

PLOS ONE

Dear Dr. Salani,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

Dr. Junming Yue

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Number of experimental reads of the predominant mature form of each miRNA.

(DOCX)

pone.0296198.s001.docx^{(5.9KB, docx)}

S2 Table. Fifty-nine differentially expressed miRNAs (2-fold) in HCC vs normal livers according to OMCD.

(XLSX)

pone.0296198.s002.xlsx^{(8.3KB, xlsx)}

S1 Fig. KEGG HCC pathways.

Enriched genes from target up- or down-regulated miRNAs are red-highlighted.

(TIF)

pone.0296198.s003.tif^{(198.4KB, tif)}

S2 Fig. Kaplan Meier plots showing prognostic significance for OS of each miRNA of the signature.

(TIF)

pone.0296198.s004.tif^{(210.2KB, tif)}

Data Availability Statement

Data are publicly available from the repositories referenced in the manuscript.

[pone.0296198.ref001] 1.McGlynn KA, Petrick JL, El-Serag HB. Epidemiology of Hepatocellular Carcinoma. Hepatology. 2021. Jan;73(Suppl 1):4–13. doi: 10.1002/hep.31288 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref002] 2.Ito T, Nguyen MH. Perspectives on the Underlying Etiology of HCC and Its Effects on Treatment Outcomes. Journal of Hepatocellular Carcinoma. 2023;10:413. doi: 10.2147/JHC.S347959 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref003] 3.Reig M, Forner A, Rimola J, Ferrer-Fàbrega J, Burrel M, Garcia-Criado Á, et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. Journal of Hepatology. 2022. Mar 1;76(3):681–93. doi: 10.1016/j.jhep.2021.11.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref004] 4.Tümen D, Heumann P, Gülow K, Demirci CN, Cosma LS, Müller M, et al. Pathogenesis and Current Treatment Strategies of Hepatocellular Carcinoma. Biomedicines. 2022. Dec;10(12):3202. doi: 10.3390/biomedicines10123202 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref005] 5.Naeli P, Winter T, Hackett AP, Alboushi L, Jafarnejad SM. The intricate balance between microRNA-induced mRNA decay and translational repression. The FEBS Journal. 2023;290(10):2508–24. doi: 10.1111/febs.16422 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref006] 6.Smolarz B, Durczyński A, Romanowicz H, Szyłło K, Hogendorf P. miRNAs in Cancer (Review of Literature). Int J Mol Sci. 2022. Mar 3;23(5):2805. doi: 10.3390/ijms23052805 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref007] 7.Li J, Bao H, Huang Z, Liang Z, Wang M, Lin N, et al. Little things with significant impact: miRNAs in hepatocellular carcinoma. Frontiers in Oncology [Internet]. 2023. [cited 2023 Nov 29];13. Available from: https://www.frontiersin.org/articles/10.3389/fonc.2023.1191070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref008] 8.Chen Z, Zhang L, Ding C, Ren K, Wan D, Lin S. A six-miRNA signature as a novel biomarker for improving prediction of prognosis and patterns of immune infiltration in hepatocellular carcinoma. Am J Transl Res. 2022. Jun 15;14(6):3610–37. [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref009] 9.A five‐miRNA expression signature predicts survival in hepatocellular carcinoma—Liu—2017—APMIS—Wiley Online Library [Internet]. [cited 2023 Nov 29]. Available from: https://onlinelibrary.wiley.com/doi/10.1111/apm.12697 [DOI] [PubMed]

[pone.0296198.ref010] 10.Yerukala Sathipati S., Ho SY. Novel miRNA signature for predicting the stage of hepatocellular carcinoma. Sci Rep 10, 14452 (2020). doi: 10.1038/s41598-020-71324-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref011] 11.Sarver AL, Sarver AE, Yuan C, Subramanian S. OMCD: OncomiR Cancer Database. BMC Cancer. 2018. Dec 6;18(1):1223. doi: 10.1186/s12885-018-5085-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref012] 12.Discovery Győrffy B. and ranking of the most robust prognostic biomarkers in serous ovarian cancer. GeroScience. 2023. Mar 1;45(3):1889–98. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref013] 13.Kuo WT, Su MW, Lee YL, Chen CH, Wu CW, Fang WL, et al. Bioinformatic Interrogation of 5p-arm and 3p-arm Specific miRNA Expression Using TCGA Datasets. J Clin Med. 2015. Sep 15;4(9):1798–814. doi: 10.3390/jcm4091798 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref014] 14.Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019. Jan 8;47(Database issue):D155–62. doi: 10.1093/nar/gky1141 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref015] 15.Huang HY, Lin YCD, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions. Nucleic Acids Res. 2021. Nov 30;50(D1):D222–30. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref016] 16.Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Research. 2022. Jul 5;50(W1):W216–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref017] 17.Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Research. 2021. Jan 8;49(D1):D545–51. doi: 10.1093/nar/gkaa970 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref018] 18.Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Research. 2019. Jul 2;47(W1):W199–205. doi: 10.1093/nar/gkz401 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref019] 19.Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018. Apr 5;173(2):371–385.e18. doi: 10.1016/j.cell.2018.02.060 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref020] 20.Lin J, Zhang H, Yu H, Bi X, Zhang W, et al. Epidemiological Characteristics of Primary Liver Cancer in Mainland China From 2003 to 2020: A Representative Multicenter Study. Front Oncol. 2022. Jun 21;12:906778. doi: 10.3389/fonc.2022.906778 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref021] 21.Watahiki A, Macfarlane RJ, Gleave ME, Crea F, Wang Y, Helgason CD, et al. Plasma miRNAs as Biomarkers to Identify Patients with Castration-Resistant Metastatic Prostate Cancer. International Journal of Molecular Sciences. 2013. Apr;14(4):7757–70. doi: 10.3390/ijms14047757 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref022] 22.Yu W, Deng W, Zhao Q, Zhuang H, Zhang C, Jian Z. miR-501 acts as an independent prognostic factor that promotes the epithelial–mesenchymal transition through targeting JDP2 in hepatocellular carcinoma. Human Cell. 2019. Jul 1;32(3):343–51. doi: 10.1007/s13577-019-00243-7 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref023] 23.Jiang J, Zhang Y, Guo Y, Yu C, Chen M, Li Z, et al. MicroRNA-3127 promotes cell proliferation and tumorigenicity in hepatocellular carcinoma by disrupting of PI3K/AKT negative regulation. Oncotarget. 2015. Jan 31;6(8):6359–72. doi: 10.18632/oncotarget.3438 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref024] 24.Zhou X, Zhu H qiang, qun Ma C, Li H guang, Liu F feng, Chang H, et al. MiR-1180 promoted the proliferation of hepatocellular carcinoma cells by repressing TNIP2 expression. Biomedicine & Pharmacotherapy. 2016. Apr 1;79:315–20. doi: 10.1016/j.biopha.2016.02.025 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref025] 25.He H, Zhou J, Cheng F, Li H, Quan Y. MiR-3677-3p promotes development and sorafenib resistance of hepatitis B-related hepatocellular carcinoma by inhibiting FOXM1 ubiquitination. Human Cell. 2023. Sep 1;36(5):1773–89. doi: 10.1007/s13577-023-00945-z [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref026] 26.Huang X, Qin J, Lu S. Up-regulation of miR-877 induced by paclitaxel inhibits hepatocellular carcinoma cell proliferation though targeting FOXM1. Int J Clin Exp Pathol. 2015. Feb 1;8(2):1515–24. [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref027] 27.Wang H, Wang B, Wang T, Fan R. A genetic variant in the promoter region of miR-877 is associated with an increased risk of hepatocellular carcinoma. Clinics and Research in Hepatology and Gastroenterology. 2020. Oct 1;44(5):692–8. doi: 10.1016/j.clinre.2020.01.006 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref028] 28.Qin Y, Ashrafizadeh M, Mongiardini V, Grimaldi B, Crea F, Rietdorf K, et al. Autophagy and cancer drug resistance in dialogue: Pre-clinical and clinical evidence. Cancer Letters. 2023. Aug 28;570:216307. doi: 10.1016/j.canlet.2023.216307 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref029] 29.Zhang L, Zhang T, Deng Z, Sun L. MicroRNA-3653 inhibits the growth and metastasis of hepatocellular carcinoma by inhibiting ITGB1. Oncology Reports. 2019. Mar 1;41(3):1669–77. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref030] 30.Wong CC, Wong C, Tung EK, Au SL, Lee JM, Poon RT, et al. The MicroRNA miR-139 Suppresses Metastasis and Progression of Hepatocellular Carcinoma by Down-regulating Rho-Kinase 2. Gastroenterology. 2011. Jan 1;140(1):322–31. doi: 10.1053/j.gastro.2010.10.006 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref031] 31.Liu C, Xu K, Liu J, He C, Liu P, Fu Q, et al. LncRNA RP11-620J15.3 promotes HCC cell proliferation and metastasis by targeting miR-326/GPI to enhance glycolysis. Biol Direct. 2023. Apr 5;18:15. doi: 10.1186/s13062-023-00370-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref032] 32.Lai Y, Liu J, Hu X, Zeng X, Gao P. Modifications of The Human Liver Cancer Cells through microRNA-145-Mediated Targeting of CDCA3. Cell J. 2023. Aug;25(8):546–53. doi: 10.22074/cellj.2023.1995666.1251 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref033] 33.Wu CS, Chien YC, Yen C, Wu JY, Bai LY, Yu YL. EZH2-mediated epigenetic silencing of tumor-suppressive let-7c/miR-99a cluster by hepatitis B virus X antigen enhances hepatocellular carcinoma progression and metastasis. Cancer Cell International. 2023. Sep 9;23(1):199. doi: 10.1186/s12935-023-03002-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref034] 34.Crea F, Fornaro L, Bocci G, Sun L, Farrar WL, Falcone A, et al. EZH2 inhibition: targeting the crossroad of tumor invasion and angiogenesis. Cancer Metastasis Rev. 2012. Dec 1;31(3):753–61. doi: 10.1007/s10555-012-9387-3 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref035] 35.Salani F, Latarani M, Casadei-Gardini A, Gangadharannambiar P, Fornaro L, Vivaldi C, et al. Predictive significance of circulating histones in hepatocellular carcinoma patients treated with sorafenib. Epigenomics. 2022. May;14(9):507–17. doi: 10.2217/epi-2021-0383 [DOI] [PubMed] [Google Scholar]

[pone.0296198.ref036] 36.Juracek J, Madrzyk M, Trachtova K, Ruckova M, Bohosova J, et al. Combination of Urinary MiR-501 and MiR-335 With Current Clinical Diagnostic Parameters as Potential Predictive Factors of Prostate Biopsy Outcome. Cancer Genomics Proteomics. 2023. May-Jun;20(3):308–316. doi: 10.21873/cgp.20383 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref037] 37.Guo Y, Zhang X, Wang L, Li M, Shen M, et al. The plasma exosomal miR-1180-3p serves as a novel potential diagnostic marker for cutaneous melanoma. Cancer Cell Int. 2021. Sep 20;21(1):487. doi: 10.1186/s12935-021-02164-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296198.ref038] 38.Sun Y, Chen C, Zhang P, Xie H, Hou L, et al. Reduced miR-3127-5p expression promotes NSCLC proliferation/invasion and contributes to dasatinib sensitivity via the c-Abl/Ras/ERK pathway. Sci Rep. 2014. Oct 6;4:6527. doi: 10.1038/srep06527 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Comprehensive analysis of differentially expressed miRNAs in hepatocellular carcinoma: Prognostic, predictive significance and pathway insights

Kayleigh Smith

Dan Beach

Roger Silva

Gyorffy Balazs

Francesca Salani

Francesco Crea

Roles

Abstract

1. Introduction

2. Material and methods

2.1 Differential expression analysis

2.2 Survival analysis

2.3 Construction of a prognostic miRNA signature

2.4 Pathway analysis

2.4.1 Selection of mature miRNAs

2.4.2 Target gene identification

2.4.3 Functional annotation and enrichment analysis

2.4.4 Analysis of enriched KEGG pathways

2.4.5 Validation of the functional enrichment analysis

2.4.6 Identifying potential miRNA interactions with cancer driver genes

3. Results

3.1 Some differentially expressed miRNAs are prognostic in resected HCC

Table 1. OS correlation of differentially expressed miRNAs (> 2-fold increase or decrease in HCC vs normal livers), according to KMP; miRNAs in bold are significantly correlated with OS.

Fig 1.

3.2 Functional pathways of miRNA-targeted genes

Table 2. HCC-specific pathways relative to up-regulated miRNAs.

Table 3. HCC-specific pathways relative to down-regulated miRNAs.

Fig 2. Enrichment analysis for miRNA-targeted genes in KEGG pathways.

Table 4. HCC- and pan-cancer driver genes among miRNA target genes.

Fig 3. HCC-specific miRNA-targeted genes significantly associated with overall survival in the sorafenib-treated cohort from Kaplan Meier plotter.

4. Discussion

Supporting information

Data Availability

Funding Statement

References

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Junming Yue

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Author response to Decision Letter 0

Decision Letter 1

Junming Yue

Roles

Author response to Decision Letter 1

Decision Letter 2

Junming Yue

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Acceptance letter

Junming Yue

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Associated Data

Supplementary Materials

Data Availability Statement

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