In silico analysis of lncRNA-miRNA-mRNA signatures related to Sorafenib effectiveness in liver cancer cells

WJG https://www.wjgnet.com 1January 21, 2025 Volume 31 Issue 3 World Journal of Gastroenterology W J G Submit a Manuscript: https://www.f6publishing.com World J Gastroenterol 2025 January 21; 31(3): 95207 DOI: 10.3748/wjg.v31.i3.95207 ISSN 1007-9327 (print) ISSN 2219-2840 (online) ORIGINAL ARTICLE Basic Study In silico analysis of lncRNA-miRNA-mRNA signatures related to Sorafenib effectiveness in liver cancer cells Patricia de la Cruz-Ojeda, Ester Parras-Martínez, Raquel Rey-Pérez, Jordi Muntané Specialty type: Gastroenterology and hepatology Provenance and peer review: Unsolicited article; Externally peer reviewed. Peer-review model: Single blind Peer-review report’s classification Scientific Quality: Grade C Novelty: Grade B Creativity or Innovation: Grade B Scientific Significance: Grade B P-Reviewer: Wang W Received: April 4, 2024 Revised: August 30, 2024 Accepted: September 12, 2024 Published online: January 21, 2025 Processing time: 259 Days and 20.4 Hours Patricia de la Cruz-Ojeda, Functional Genomics of Solid Tumors Laboratory, Centre de Recherche des Cordeliers, Paris 75006, France Patricia de la Cruz-Ojeda, Ester Parras-Martínez, Raquel Rey-Pérez, Jordi Muntané, Department of Oncology Surgery, Cell Therapy and Organ Transplantation, Institute of Biomedicine of Seville, Virgen del Rocio University Hospital, Seville 41013, Spain Patricia de la Cruz-Ojeda, Jordi Muntané, Biomedical Research Center for Hepatic and Digestive Diseases, CIBERehd, Madrid 28029, Spain Jordi Muntané, Department of Medical Physiology and Biophysics, University of Seville, Seville 41009, Spain Corresponding author: Jordi Muntané, PhD, Department of Oncology Surgery, Cell Therapy and Organ Transplantation, Institute of Biomedicine of Seville, Virgen del Rocio University Hospital, Avn. Manuel Siurot S/N, Seville 41013, Spain. [email protected] Abstract BACKGROUND Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer with varied incidence and epidemiology worldwide. Sorafenib is still a recommended treatment for a large proportion of patients with advanced HCC. Different patterns of treatment responsiveness have been identified in differentiated hepatoblastoma HepG2 cells and metastatic HCC SNU449 cells. AIM To define the long non-codingRNA-microRNA-mRNA (lncRNA-miRNA-mRNA) predicted signatures related to selected hallmarks of cancer (apoptosis, autophagy, cell stress, cell dedifferentiation and invasiveness) in RNAseq studies using Sorafenib-treated HepG2 and SNU449 cells. Various available software analyses allowed us to establish the lncRNA-miRNA-mRNA regulatory axes following treatment in HepG2 and SNU449 cells. METHODS HepG2 and SNU449 cells were treated with Sorafenib (10 μmol/L) for 24 hours. Total RNA, including small and long RNA, was extracted with a commercial miRNeasy kit. RNAseq was carried out for the identification of changes in lncRNA-miRNA-mRNA regulatory axes. de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 2January 21, 2025 Volume 31 Issue 3 RESULTS MALAT, THAP9-AS1 and SNGH17 appeared to coordinately regulate miR-374b-3p and miR-769-5p that led to upregulation of SMAD7, TIRARP, TFAP4 and FAXDC2 in HepG2 cells. SNHG12, EPB41 L4A-AS1, LINC01578, SNHG12 and GAS5 interacted with let-7b-3p, miR-195-5p and VEGFA in SNU449 cells. The axes MALAT1/hsamir-374b-3p/SMAD7 and MALAT1/hsa-mir-769-5p/TFAP4 were of high relevance for Sorafenib response in HepG2 cells, whereas PVT1/hsa-miR-195-5p/VEGFA was responsible for the differential response of SNU449 cells to Sorafenib treatment. CONCLUSION Critical lncRNAs acting as sponges of miRNA were identified that regulated mRNA expression, whose proteins mainly increased the antitumor effectiveness of the treatment (SMAD7, TIRARP, TFAP4, FAXDC2 and ADRB2). However, the broad regulatory axis leading to increased VEGFA expression may be related to the side effect of Sorafenib in SNU449 cells. Key Words: Cell culture; Hepatocellular carcinoma; Non-coding RNA; RNAseq; Sorafenib ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. Core Tip: In the current study, we investigated the differential lncRNA-miRNA-mRNA regulatory axes in HepG2 and SNU449 under Sorafenib treatment. The study identified lncRNA-miRNA regulatory axes leading to increased expression mRNA with positive (SMAD7, TIRARP, TFAP4, FAXDC2 and ADRB2) and negative (VEGFA) therapeutic effects in HepG2 and SNU449 under Sorafenib treatment. Citation: de la Cruz-Ojeda P, Parras-Martínez E, Rey-Pérez R, Muntané J. In silico analysis of lncRNA-miRNA-mRNA signatures related to Sorafenib effectiveness in liver cancer cells. World J Gastroenterol 2025; 31(3): 95207 URL: https://www.wjgnet.com/1007-9327/full/v31/i3/95207.htm DOI: https://dx.doi.org/10.3748/wjg.v31.i3.95207 INTRODUCTION Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for 75%-86% of cases[1]. The impact of risk factors on HCC has a variable geographic distribution, including hepatitis C virus and hepatitis B virus infection, alcohol, aflatoxin B1, metabolic-associated fatty liver disease (MAFLD), tobacco and congenital diseases. Furthermore, only one third of HCC patients are diagnosed at the initial stages susceptible to receiving curative treatment, the remaining patients are at intermediate/advanced stage with poor prognosis and a 5-year survival rate below 10%[2]. The Barcelona Clinic Liver Cancer classification considers the number and size of tumor nodules, liver function, general condition of the patient and the presence of vascular invasiveness and metastases[3]. According to this classification, HCC stage C includes patients with preserved liver function but with vascular invasion and/or the presence of extrahepatic nodules. The landscape of treatments for advanced stage HCC has been changing due to the approval of Atezolizumab-Bevacizumab and Durvalumab-Tremelimumab as first-line therapies, extending the median survival up to 19.2 months[3]. However, patients without preserved liver function, a high-risk of bleeding, vascular disorders, and arterial hypertension, as well as severe autoimmune disorders and prior transplantation are not recommended to receive immune checkpoint and antiangiogenic treatments[3]. Furthermore, subgroup analysis of several phase III clinical trials suggests that Sorafenib tends to be more effective than immune checkpoint inhibitor-based therapy in patients with non-viral etiologies (alcohol, MAFLD or unknown)[4]. Sorafenib is an orally administered multityrosine kinase inhibitor approved by the FDA for the treatment of HCC in 2008[5,6]. It targets several proliferation receptors such as vascular endothelial growth factor (VEGFR)-1, VEGFR-2, VEGFR-3, platelet-derived growth factor receptor (PDGFR)-β, c-KIT, FLT-3 and RET. Furthermore, it also inhibits downstream kinases with serine/threonine phosphorylating activity[7-11]. Sorafenib induces early endoplasmic reticulum stress with activation of the JNK/AMPK/autophagy-dependent pathway that leads to a switch towards apoptotic cell death in HepG2 cells[7]. Given that Sorafenib exerts a greater proapoptotic activity in HepG2 cells than in SNU449 cells[12], a link between treatment resistance processes and induction of the epithelial-mesenchymal transition (EMT) process has also been demonstrated. In this regard, SNU449, HLF and HLE liver cancer cell lines expressing mesenchymal markers (CD44, Vimentin and Snail) are refractory to Sorafenib treatment compared to HepG2, Hep3B and PLC/PRF/5, which express epithelial markers (E-cadherin and CK-18)[13]. Sorafenib resistance might also involve overexpression of ATP-binding box (ABC) transporters that export drugs and reduce the efficacy of treatment[14]. The human genome is made up of 70% intergenic regions, while genes account for only 30%. Among non-coding RNAs (ncRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) have been related to the initiation, progression and metastasis of HCC[15]. In broad terms, miRNAs are a type of ncRNAs, 22 nucleotides in length, which bind to the complementary sequence of the mRNA and induce its degradation mediated by de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 3January 21, 2025 Volume 31 Issue 3 the RNA-Induced Silencing Complex. Although several miRNA signatures have been associated with the diagnosis of HCC, few models have assessed the risk associated with treatment effectiveness[16,17]. We have recently identified miR200c-3p, miR-222-5p, and miR-512-3p as prognostic miRNA markers in patients under Sorafenib treatment[18]. On the other hand, lncRNAs and circRNAs are more than 200 nucleotides long; however, lncRNAs are linear, while circRNAs are ring-shaped. Both can be transcribed from exons, introns, intergenic regions or 5′/3′ non-translational regions and folded into complicated second structures, facilitating their interactions with DNA, RNA, and proteins. lncRNAs have been implicated in the regulation of transcriptional activity, and they are specifically expressed in cells in response to numerous stimuli and serve as molecular signals[19]. They can also act as scaffolds, central platforms on which the relevant molecular components will be assembled[20]. Given that Sorafenib exerts a greater proapoptotic activity in HepG2 cells than in SNU449 cells[12], the present study aimed to determine a treatment-associated lncRNA profile directly correlated to differential signatures of lncRNAsmiRNA-mRNAs related to apoptosis, autophagy, endoplasmic reticulum stress, differentiation, and metastasis in RNAseq data obtained from Sorafenib-treated HepG2 and SNU449 cells. MATERIALS AND METHODS Cell culture The study involved the differentiated hepatoblastoma cell line HepG2 (HB-8065™, ATCC-LGC Standards, S.L.U., Barcelona, Spain) and the metastatic HCC SNU449 cell line (CRL-2234 HB-8065™, ATCC-LGC Standards). Cells were cultured in MEM containing Earle's salts with L-glutamine and 10% FBS (F7524, lot BCBX9154, Merck-Sigma, St. Louis, MO, United States), sodium pyruvate (1 mmol/L) (Ref. 11360070, Thermo Fisher Scientific, Waltham, MA, United States), non-essential amino acids (Ref. 11140035, Thermo Fisher Scientific), penicillin-streptomycin solution (100 U/mL-100 μg/ mL) (Ref. 15640055, Thermo Fisher Scientific) at 37 °C, 21% O2 and 5% CO2. Cells were plated at 100000 cells/cm2, and after 24 hours of stabilization Sorafenib (10 μM) (FS10808, Carbosynth, United Kingdom) was added. Cells were harvested at 6 and 24 hours after Sorafenib treatment. Sorafenib dose, treatment times and response were performed as optimized in the study by Rodríguez-Hernández et al[7]. RNA-seq Total RNA, including small and long RNA, was extracted with the miRNeasy kit (Ref. 217004, Qiagen, Hilden, Germany). Lysis was performed with Qiazol and the RNA fraction was bound to the RNeasy mini spin column, washed, and eluted in RNase-free water. The DNA was removed by DNase I digestion. RNA was then quantified using the NanoDrop™ One/OneC microvolume UV-Vis UV-Vis spectrophotometer (Thermo Fisher Scientific). RNA was quantified using the RNA Qubit™ HS assay (Q32852, Thermo Fisher Scientific), and the quality was assessed with the Bioanalyzer® 2100 Eukaryote Total RNA Nano Chip (Agilent Technologies, Santa Clara, CA, United States). All samples had an RNA Integrity Number ≥ 8.8. Total RNA libraries were prepared with the Illumina Stranded Total RNA Prep Ligation with Ribo-Zero™ Plus kit (Ref. 20040529, Illumina Inc., San Diego, CA, United States). Briefly, rRNA was depleted and the total transcriptome was fragmented. cDNA was synthesized, adapters and dual indices were joined and then the library was amplified. The libraries were then quantified with the Qubit™ DNA HS assay and the quality was analyzed with the highly sensitive Bioanalyzer® 2100 DNA chip (Agilent Technologies). The size of the total RNA libraries was 260-280 bp. They were pooled at 2.4 nM, denatured and diluted to 1.2 nM. PhiX was used as an internal quality control. Paired-end sequencing (75 cycles) was performed in S1 flow cells on a NovaSEq 6000 instrument (Illumina. Inc.). Analysis of RNA-seq data Long RNA-seq data analysis: Primary analysis of RNA sequencing data, including quality testing, trimming and demultiplexing, was conducted using the Illumina DRAGEN FASTQ Generation v.3.8.4 app of BaseSpace. Next, alignment to the human genome version GRCh38 was performed with the RNA-Seq Alignment tool v.2.02, using the STAR aligner. Differential expression analysis (DEA) was carried out with the DRAGEN Differential Expression app v.3.9.0 by running the DESeq2 algorithm[21]. BioMart data mining tool (Ensembl)[22] was used to determine gene type. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways annotation and enrichment of protein coding genes was performed with Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. Gene Set Enrichment Analysis (GSEA) was carried out with the Hallmark and GO Biological Process gene sets[23,24]. Small RNA-seq data analysis: Primary analysis of small RNA sequencing data, including quality testing, trimming and demultiplexing, was conducted using the Illumina DRAGEN FASTQ Generation v.3.8.4 app of BaseSpace. Next, the adaptor sequence was eliminated, and data were filtered by size of 23 bp, taking into account a minimum size insert of 15 bp. Also, readings were cut first and last 4 nucleotides, following the manufacturer’s instructions. Mapping and alignment to the human genome version GRCh38 using bowtie aligner was performed in Galaxy RNA[25] environment with the miRDeep Mapper tool[26]. Quantitation of reads mapping to known miRBase hairpin miRNA precursor and mature miRNA sequences was done with the miRDeep Quantifier tool[26]. Finally, DEA was performed with R Studio using the DSeq2 package. Heatmaps were constructed using R Studio with ComplexHeatmap, viridis and dplyr packages. GO terms and KEGG pathways enrichment analysis was performed with the DAVID online tool[27] using all genes with counts > 0 as background. Biological connections among selected targets were estimated using Search Tool for the Retrieval of de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 4January 21, 2025 Volume 31 Issue 3 Interacting Genes/Proteins (STRING)[28]. STRING analysis was performed with medium confidence (interaction score 0.4). Validation of proposed interacting networks based on tumor and non-tumor liver tissue expression data was performed using GEPIA using the Correlation tool (Pearson method)[29]. In silico establishment of lncRNA-miRNA-mRNA regulatory axis lncRNA as a sponge of miRNAs: mRNAs of relevance in Sorafenib response were selected among GSEA GO Biological Process terms related to apoptosis, autophagy, endoplasmic reticulum stress, differentiation, and metastasis (including development and angiogenesis related terms). Moderately and highly expressed mRNAs in relevant significantly enriched GO terms in GSEA with log2FC > 1 or log2FC < -1 and P-adj < 0.05 were used as candidates of miRNA regulation. miRNAs that could target these candidates were predicted with the miRDB database, with a cut-off of Target Score ≥ 80. From this miRNA list, we selected those that were downor up-regulated accordingly in our study. Then, miRNA-lncRNA interactions were predicted with DIANA-LncBase v3 (DIANA tools)[30,31]. lncRNAs significantly upregulated in our study were used for further analysis. lncRNA-mRNA interactions to regulate stability: mRNAs previously selected to be regulated by miRNAs were used to test possible interactions with regulatory lncRNAs in our study as described above. LncRRI search web server was used to test interactions with an energy threshold of -12Kcal/mol[32], using specific transcripts. lncRNA-protein interactions: Finally, to identify lncRNA interactions with proteins whose mRNA is regulated in our study we used the RNAct tool[33], using both predicted and experimentally confirmed targets. RESULTS Sorafenib exerts differential anti-tumor properties in different cell lines. Whereas HepG2 cells are considered to be sensitive to Sorafenib, the poorly-differentiated SNU449 cell line showed a lower pattern of treatment response. Therefore, we investigated RNA profiles to unravel potential candidates of Sorafenib response in vitro. First, to identify pathways related to differential treatment response in HepG2 and SNU449 cells, we selected differentially expressed (DE) genes with a base mean > 30, log2FC > 0.5 (upregulated) or < -0.5 (downregulated) and P-adjusted (P-adj) < 0.05 for Venn analysis. The study of long RNA species identified 8345 DE genes, among which 243 RNAs were common to all conditions studied, indicating a common mechanism of Sorafenib in both cell lines (Figure 1A). Most of these genes were mRNAs coding for proteins, followed by lncRNAs (Figure 1B). Hierarchical clustering allowed for the grouping of expression profiles into 4 clusters (Figure 1C). Cluster 1 corresponded with genes that were up-regulated in response to Sorafenib, and this effect was more prominent in HepG2 cells compared to SNU449 cells. Cluster 2 was composed of genes up-regulated in SNU449 cells, whereas cluster 3 was representative of genes with higher expression in HepG2 cells. Cluster 4 involved down-regulated genes after Sorafenib treatment. As in the case of cluster 1, the effect of Sorafenib was more prominent in HepG2 cells. These results confirmed the differential effectiveness of Sorafenib in these cells lines. GO term and KEGG pathway analysis was performed with genes of each cluster (Figure 1D-G). Cluster 1 was focused on the cell nucleus, controlling transcription and RNA processing (Figure 1D). Genes with increased expression in SNU449 cells (cluster 2) were involved in the regulation of focal adhesions, extracellular matrix and pathways involving MAPK and serine-threonine protein kinase activity (Figure 1E). On the other hand, cluster 3 that contained genes with higher expression in HepG2 cells were related to lipid, carbon and amino acid metabolism, mitochondria, and EMT (Figure 1F). Cluster 4, which was downregulated in response to Sorafenib, was involved in DNA repair and epigenetic chromatin remodeling processes (Figure 1G). Next, we sequenced small RNA species to identify a miRNA pattern related to Sorafenib response in each cell line. DE genes with a base mean > 15, log2FC > 0.5 (upregulated) or < -0.5 (downregulated) and P-value < 0.05 were selected. In this case, we found 110 DE miRNAs in HepG2 and SNU449 cells (Figure 2A). miRNAs could also be clustered according to expression profiles (Figure 2B). Cluster 1 contained miRNAs with higher expression in SNU449 cells. Cluster 2 was upregulated in response to Sorafenib. Cluster 3 was more heterogeneous and included miRNAs that were slightly upregulated in HepG2 cells. Cluster 4 included miRNAs with higher expression in HepG2 cells. Next, we investigated whether the differences in Sorafenib response in HepG2 and SNU449 cells were due to a differential RNA integration network. Therefore, we determined regulatory loops among mRNAs, lncRNAs and miRNAs. In this sense, we explored the role of lncRNAs as sponges of miRNAs that regulate mRNA expression, as well as their role in controlling mRNAs and lncRNA-protein interactions. We performed a targeted search of mRNAs according to previously described Sorafenib anti-tumor properties. We selected mRNAs from significantly enriched GSEA terms related to apoptosis, autophagy, endoplasmic reticulum stress, differentiation, and metastasis, including differentiation and angiogenesis (Figure 3A). In HepG2 cells, Sorafenib induced the deregulation of terms related to cell death induction (endoplasmic reticulum stress, apoptosis, autophagy) more prominently at 24 hours of treatment (34/118, 28.8%), compared to 6 hours (13/79, 16.4%) (Figure 3B, Supplementary Figure 1A, Supplementary Table 1). Surprisingly, Sorafenib had a greater impact on the processes selected at 6 hours than at longer treatment times in SNU449 cells. In particular, the effect on cell death was enriched at 6 hours (18/56, 32.1%), compared to 24 hours (3/28, 10.7%), when terms related to migration and invasion were dominant (Figure 3C, Supplementary Figure 1B, Supplementary Table 1). A total of 441 mRNAs related to Sorafenib properties were deregulated in this study, whose expression is shown in Figure 3D. de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 5January 21, 2025 Volume 31 Issue 3 Figure 1 Expression profile of long RNA species and enrichment analysis. A: Venn diagram of differentially expressed (DE) genes in HepG2 and de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 6January 21, 2025 Volume 31 Issue 3 SNU449 cells treated with Sorafenib for 6 and 24 hours; B: Pie chart showing identification of RNA species; C: Heatmap of the 8345 DE genes grouped into 4 row clusters; D: Top 4 Gene Ontology terms and Kyoto Encyclopedia of Genes and Genome pathways in cluster 1; E: Cluster 2; F: Cluster 3; G: Cluster 4 identified in the heatmap. Figure 2 Expression profile of small RNAs. A: Venn diagram of differentially expressed (DE) miRNAs in HepG2 and SNU449 cells treated with Sorafenib for 6 and 24 hours; B: Heatmap of the 110 DE miRNAs grouped into 4 row clusters. An in silico prediction of miRNAs targeting selected mRNAs in Figure 3D was carried out to search for sources of posttranscriptional mRNA regulation (Figure 4A and B; Supplementary Figure 2). From these miRNAs, we selected for further analysis those that were DE in our study with a P-value < 0.05 and log2FC < -1 or > 1 (Figure 4C). A total of 49 putative miRNA-mRNA interactions were identified, in which 23 unique miRNAs participated. Different miRNA profiles were observed in each condition, being hsa-miR-19b-3p and hsa-miR-29b-3p specific to HepG2 cells treated with Sorafenib at 6 hours, compared to 24 hours, a timepoint that involved the specific regulation of hsa-miR-27b-5p, hsa-miR193b-3p, hsa-miR-194-3p, hsa-miR-374a-3p, hsa-miR-374b-3p, hsa-miR-769-5p, hsa-miR-3187-3p, hsa-miR-4488, hsa-miR4521 and hsa-miR-7974. HRK, JMY, TNFAIP3, RORA and ZNF385B mRNAs were specifically regulated at 6 hours of Sorafenib treatment, whereas MBNL1, CREB3 L3, ARHGAP24, HOXA1, SH3GLB1, OXR1, GOLGA4, OPTN, SIAH1, LAMC2, PLK2, ATOH8, SMAD7, TIRARP, TFAP4, FAXDC2, and MELTF were specific to 24 hours. ARRDC3 was the only common mRNA in HepG2 cells at 6 and 24 hours of treatment. Although both cell lines shared miR-374 family (hsa-miR374c-5p in SNU499 cells), hsa-miR-4488 was the only common miRNA shared by treatments at 24 hours, constituting hsalet-7b-3p, hsa-miR-29b-1-5p, hsa-miR-32-3p, hsa-miR-34a-5p, hsa-miR-195-5p, hsa-miR-12136, hsa-miR-4492 and hsa-miR5701 a specific signature of SNU449 cells (24 hours). Coherently, no mRNAs were common in both cell lines at 24 hours. HEY1, CEBPB, VEGFA, ADM, YOD1, TNFAIP3, KLF4, ADRB2, HSPA1B, SYNGR3, RFLNB, ACVR2B, NR4A2, CSPG5, AREG, NRARP, MAF, IGFBP5, and PDGFB were specifically related to SNU449 cells (24 hours). As in the case of HepG2 cells, Sorafenib at 6 hours had a low effect on miRNA-mRNA based interactions (hsa-miR-15a-5p, hsa-miR-12135) (CHAC1, FOXJ1) (Table 1). The next step in the study consisted of predicting plausible interactions of lncRNAs with selected miRNAs in Figure 4C using DIANA-LncBase v3 (DIANA tools) (Figure 5A and B). From total predictions, only significantly deregulated lncRNA with coherent expression with target miRNAs were selected for further analysis (log2FC > 1, P-adj < 0.05). No downregulated lncRNAs by Sorafenib were found to be coherently altered. Nineteen upregulated lncRNAs were selected, de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 7January 21, 2025 Volume 31 Issue 3 Table 1 miRNA-mRNA interactions observed in HepG2 and SNU449 cells treated with Sorafenib for 6 and 24 hours Condition hsa-miR log2 (Fold-change) P value P-adj mRNA target Transcript ID log2 (Fold-change) P-adj HepG2 6 hours hsa-miR-29b-3p -1.374 3.23E-02 0.9961 HRK ENSG00000135116.9 1.967 8.926E-08 HepG2 6 hours hsa-miR-29b-3p -1.374 3.23E-02 0.9961 JMY ENSG00000152409.9 1.105 2.253E-13 HepG2 6 hours hsa-miR-29b-3p -1.374 3.23E-02 0.9961 TNFAIP3 ENSG00000118503.15 1.186 4.131E-07 HepG2 6 hours hsa-miR-19b-3p -1.277 4.29E-02 0.9961 RORA ENSG00000069667.16 1.048 1.506E-03 HepG2 6 hours hsa-miR-19b-3p -1.277 4.29E-02 0.9961 TNFAIP3 ENSG00000118503.15 1.186 4.131E-07 HepG2 6 hours hsa-miR-19b-3p -1.277 4.29E-02 0.9961 ZNF385B ENSG00000144331.20 1.159 1.233E-02 HepG2 6 hours hsa-miR-19b-3p -1.277 4.29E-02 0.9961 ARRDC3 ENSG00000113369.9 1.697 4.636E-30 HepG2 24 hours hsa-miR-4521 -4.496 1.09E-04 0.1484 MBNL1 ENSG00000152601.17 1.242 2.599E-16 HepG2 24 hours hsa-miR-7974 -3.432 1.97E-42 0.0000 CREB3 L3 ENSG00000060566.14 1.062 4.001E-06 HepG2 24 hours hsa-miR-194-3p -1.463 2.73E-08 0.0000 ARHGAP24 ENSG00000138639.18 1.281 1.186E-08 HepG2 24 hours hsa-miR-194-3p -1.463 4.47E-05 0.0020 HOXA1 ENSG00000105991.9 1.192 2.484E-06 HepG2 24 hours hsa-miR-374a-3p -1.327 4.47E-05 0.0020 SH3GLB1 ENSG00000097033.14 1.296 1.800E-13 HepG2 24 hours hsa-miR-374a-3p -1.327 8.95E-03 0.1859 OXR1 ENSG00000164830.18 1.050 2.089E-08 HepG2 24 hours hsa-miR-374a-3p -1.327 8.95E-03 0.1859 GOLGA4 ENSG00000144674.16 1.230 8.419E-03 HepG2 24 hours hsa-miR-374a-3p -1.327 8.95E-03 0.1859 ARRDC3 ENSG00000113369.9 2.849 2.203E-41 HepG2 24 hours hsa-miR-27b-5p -1.236 8.95E-03 0.1859 OPTN ENSG00000123240.17 1.531 6.346E-20 HepG2 24 hours hsa-miR-193b-3p -1.193 3.74E-04 0.0143 SIAH1 ENSG00000196470.12 1.581 1.144E-38 HepG2 24 hours hsa-miR-193b-3p -1.193 2.29E-02 0.3864 LAMC2 ENSG00000058085.15 1.327 2.152E-03 HepG2 24 hours hsa-miR-3187-3p -1.169 2.29E-02 0.3864 PLK2 ENSG00000145632.15 1.634 3.009E-08 HepG2 24 hours hsa-miR-3187-3p -1.169 3.59E-02 0.4538 ATOH8 ENSG00000168874.13 1.691 6.502E-08 HepG2 24 hours hsa-miR-374b-3p -1.064 3.59E-02 0.4538 SMAD7 ENSG00000101665.9 1.999 1.669E-42 HepG2 24 hours hsa-miR-374b-3p -1.064 7.47E-03 0.1693 TIRARP ENSG00000163659.13 1.070 1.555E-18 HepG2 24 hours hsa-miR-769-5p -1.058 7.47E-03 0.1693 TFAP4 ENSG00000090447.12 1.010 1.161E-11 HepG2 24 hours hsa-miR-769-5p -1.058 8.79E-03 0.1859 FAXDC2 ENSG00000170271.11 1.565 1.249E-13 HepG2 24 hours hsa-miR-4488 9.059 8.79E-03 0.1859 MELTF ENSG00000163975.12 -1.085 2.722E-05 SNU 6 hours hsa-miR-15a-5p -1.526 9.20E-03 0.6536 CHAC1 ENSG00000128965.13 2.262 5.787E-28 SNU 6 hours hsa-miR-12135 -1.298 2.11E-02 0.6536 FOXJ1 ENSG00000129654.8 1.240 2.099E-06 SNU 24 hours hsa-miR-29b-1-5p -2.126 2.50E-05 0.0007 HEY1 ENSG00000164683.17 1.692 2.304E-07 SNU 24 hours hsa-miR-374c-5p -1.884 5.25E-03 0.0787 CEBPB ENSG00000172216.6 1.002 5.306E-17 SNU 24 hours hsa-miR-374c-5p -1.884 5.25E-03 0.0787 VEGFA ENSG00000112715.22 1.448 2.131E-45 SNU 24 hours hsa-miR-374c-5p -1.884 5.25E-03 0.0787 ADM ENSG00000148926.10 2.340 1.782E-143 SNU 24 hours hsa-miR-32-3p -1.821 9.41E-04 0.0175 YOD1 ENSG00000180667.10 1.658 1.063E-111 SNU 24 hours hsa-miR-32-3p -1.821 9.41E-04 0.0175 TNFAIP3 ENSG00000118503.15 2.316 3.471E-10 SNU 24 hours hsa-let-7b-3p -1.541 1.72E-02 0.1785 KLF4 ENSG00000136826.15 1.572 1.166E-31 SNU 24 hours hsa-let-7b-3p -1.541 1.72E-02 0.1785 ADRB2 ENSG00000169252.5 1.743 9.094E-48 SNU 24 hours hsa-miR-195-5p -1.456 1.51E-02 0.1770 VEGFA ENSG00000112715.22 1.448 2.131E-45 SNU 24 hours hsa-miR-195-5p -1.456 1.51E-02 0.1770 ADRB2 ENSG00000169252.5 1.743 9.094E-48 SNU 24 hours hsa-miR-34a-5p -1.316 2.62E-02 0.2364 KLF4 ENSG00000136826.15 1.572 1.166E-31 SNU 24 hours hsa-miR-34a-5p -1.316 2.62E-02 0.2364 HSPA1B ENSG00000204388.7 1.676 2.822E-82 SNU 24 hours hsa-miR-5701 1.535 3.44E-02 0.2740 SYNGR3 ENSG00000127561.15 -1.505 1.627E-08 SNU 24 hours hsa-miR-5701 1.535 3.44E-02 0.2740 RFLNB ENSG00000183688.4 -1.340 1.864E-27 de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 8January 21, 2025 Volume 31 Issue 3 SNU 24 hours hsa-miR-12136 3.334 4.93E-16 0.0000 ACVR2B ENSG00000114739.14 -1.075 3.211E-13 SNU 24 hours hsa-miR-12136 3.334 4.93E-16 0.0000 NR4A2 ENSG00000153234.14 -1.754 9.174E-06 SNU 24 hours hsa-miR-12136 3.334 4.93E-16 0.0000 CSPG5 ENSG00000114646.10 -1.089 1.709E-08 SNU 24 hours hsa-miR-12136 3.334 4.93E-16 0.0000 AREG ENSG00000109321.11 -1.016 2.623E-03 SNU 24 hours hsa-miR-12136 3.334 4.93E-16 0.0000 NRARP ENSG00000198435.4 -1.763 3.743E-12 SNU 24 hours hsa-miR-12136 3.334 4.93E-16 0.0000 MAF ENSG00000178573.7 -1.748 5.311E-04 SNU 24 hours hsa-miR-4492 8.427 4.75E-12 0.0000 IGFBP5 ENSG00000115461.5 -1.073 2.379E-18 SNU 24 hours hsa-miR-4488 13.091 1.08E-32 0.0000 PDGFB ENSG00000100311.17 -1.101 1.086E-51 Figure 3 Identification of mRNA candidates. A: Diagram representing the study design; B: Bubble plots of enriched GSEA terms in HepG2 cells; C: SNU449 cells treated with Sorafenib 24 hours. Bubble size is representative of gene count and color is representative of the normalized enrichment score; D: Heatmap of de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 9January 21, 2025 Volume 31 Issue 3 selected mRNAs in HepG2 and SNU449 cells. Figure 4 Identification of miRNA candidates. A: Gene regulatory network showing miRNA-mRNA target predictions in HepG2 cells; B: SNU449 cells treated with Sorafenib for 24 hours; C: Heatmap of selected miRNAs. preferentially expressed at 24 hours of Sorafenib treatment (Figure 5C). CYTOR, DANT2, MALAT1, MIR4435-2HG, PCBP2-OT1, SNHG17, THAP9-AS1 and ZFAS1 were specifically regulated in HepG2 cells, whereas AZIN1-AS1, CARMN, EPB41 L4A-AS1, GAS5, LINC01578, LMCD1-AS1, PVT1, SNHG12, SNHG20, SNHG5 and SNHG7 were specifically related to SNU499 cells (Table 2). The role of lncRNA regulating mRNA instability was next explored. To complete the lncRNA-miRNA-mRNA network, we performed our in silico analysis using as targets those mRNAs that de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 16 January 21, 2025 Volume 31 Issue 3 CONCLUSION In conclusion, this study identified differential lncRNA-miRNA regulatory axes in HepG2 and SNU449 liver cancer cells. They lead to increased expression of mRNAs with positive (SMAD7, TIRARP, TFAP4, FAXDC2 and ADRB2) and negative (VEGFA) therapeutic effects in HepG2 and SNU449 under Sorafenib treatment. FOOTNOTES Author contributions: de la Cruz-Ojeda P performed the research, contributed to the implementation of analytical tools and edited the manuscript; Parras-Martínez E and Rey-Pérez R analyzed data; Muntané J designed the research, wrote the paper, and obtained funding. All authors have read and approved the final manuscript. Supported by Instituto de Salud Carlos III (ISCiii), No. PI19/01266 and No. PI22/00857; Consejería de Salud y Familias (Junta de Andalucía), No. PI-0216-2020 and No. PIP-0215-2020; Biomedical Research Network Center for Liver and Digestive Diseases (CIBERehd) founded by the ISCIII and co-financed by European Regional Development Fund “A way to achieve Europe” ERDF. Institutional review board statement: This study is based on the use of the HepG2 and SNU449 cell lines. The study did not require an Institutional review board. Institutional animal care and use committee statement: This study is based on the use of HepG2 and SNU449 cell lines. The study did not require an Institutional Animal Care and Use Committee approval form. Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article. Data sharing statement: Data will be freely available according to the requirement of the WJG as an open-access article. Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ Country of origin: Spain ORCID number: Jordi Muntané 0000-0002-6744-1121. S-Editor: Qu XL L-Editor: Webster JR P-Editor: Zhang L REFERENCES 1Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71: 209-249 [PMID: 33538338 DOI: 10.3322/caac.21660] 2European Association for the Study of the Liver. EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis. J Hepatol 2018; 69: 406-460 [PMID: 29653741 DOI: 10.1016/j.jhep.2018.03.024] 3Reig M, Forner A, Rimola J, Ferrer-Fàbrega J, Burrel M, Garcia-Criado Á, Kelley RK, Galle PR, Mazzaferro V, Salem R, Sangro B, Singal AG, Vogel A, Fuster J, Ayuso C, Bruix J. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J Hepatol 2022; 76: 681-693 [PMID: 34801630 DOI: 10.1016/j.jhep.2021.11.018] 4Pfister D, Núñez NG, Pinyol R, Govaere O, Pinter M, Szydlowska M, Gupta R, Qiu M, Deczkowska A, Weiner A, Müller F, Sinha A, Friebel E, Engleitner T, Lenggenhager D, Moncsek A, Heide D, Stirm K, Kosla J, Kotsiliti E, Leone V, Dudek M, Yousuf S, Inverso D, Singh I, Teijeiro A, Castet F, Montironi C, Haber PK, Tiniakos D, Bedossa P, Cockell S, Younes R, Vacca M, Marra F, Schattenberg JM, Allison M, Bugianesi E, Ratziu V, Pressiani T, D'Alessio A, Personeni N, Rimassa L, Daly AK, Scheiner B, Pomej K, Kirstein MM, Vogel A, PeckRadosavljevic M, Hucke F, Finkelmeier F, Waidmann O, Trojan J, Schulze K, Wege H, Koch S, Weinmann A, Bueter M, Rössler F, Siebenhüner A, De Dosso S, Mallm JP, Umansky V, Jugold M, Luedde T, Schietinger A, Schirmacher P, Emu B, Augustin HG, Billeter A, Müller-Stich B, Kikuchi H, Duda DG, Kütting F, Waldschmidt DT, Ebert MP, Rahbari N, Mei HE, Schulz AR, Ringelhan M, Malek N, Spahn S, Bitzer M, Ruiz de Galarreta M, Lujambio A, Dufour JF, Marron TU, Kaseb A, Kudo M, Huang YH, Djouder N, Wolter K, Zender L, Marche PN, Decaens T, Pinato DJ, Rad R, Mertens JC, Weber A, Unger K, Meissner F, Roth S, Jilkova ZM, Claassen M, Anstee QM, Amit I, Knolle P, Becher B, Llovet JM, Heikenwalder M. NASH limits anti-tumour surveillance in immunotherapy-treated HCC. Nature 2021; 592: 450-456 [PMID: 33762733 DOI: 10.1038/s41586-021-03362-0] 5Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, de Oliveira AC, Santoro A, Raoul JL, Forner A, Schwartz M, Porta C, Zeuzem S, Bolondi L, Greten TF, Galle PR, Seitz JF, Borbath I, Häussinger D, Giannaris T, Shan M, Moscovici M, Voliotis D, Bruix J; SHARP Investigators Study Group. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med 2008; 359: 378-390 [PMID: 18650514 DOI: 10.1056/NEJMoa0708857] Cheng AL, Kang YK, Chen Z, Tsao CJ, Qin S, Kim JS, Luo R, Feng J, Ye S, Yang TS, Xu J, Sun Y, Liang H, Liu J, Wang J, Tak WY, Pan H, 6 de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 17 January 21, 2025 Volume 31 Issue 3 Burock K, Zou J, Voliotis D, Guan Z. Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: a phase III randomised, double-blind, placebo-controlled trial. Lancet Oncol 2009; 10: 25-34 [PMID: 19095497 DOI: 10.1016/S1470-2045(08)70285-7] 7Rodríguez-Hernández MA, González R, de la Rosa ÁJ, Gallego P, Ordóñez R, Navarro-Villarán E, Contreras L, Rodríguez-Arribas M, Gonz ález-Gallego J, Álamo-Martínez JM, Marín-Gómez LM, Del Campo JA, Quiles JL, Fuentes JM, de la Cruz J, Mauriz JL, Padillo FJ, Muntané J. Molecular characterization of autophagic and apoptotic signaling induced by sorafenib in liver cancer cells. J Cell Physiol 2018; 234: 692708 [PMID: 30132846 DOI: 10.1002/jcp.26855] 8Fernando J, Sancho P, Fernández-Rodriguez CM, Lledó JL, Caja L, Campbell JS, Fausto N, Fabregat I. Sorafenib sensitizes hepatocellular carcinoma cells to physiological apoptotic stimuli. J Cell Physiol 2012; 227: 1319-1325 [PMID: 21604268 DOI: 10.1002/jcp.22843] 9Wei JC, Meng FD, Qu K, Wang ZX, Wu QF, Zhang LQ, Pang Q, Liu C. Sorafenib inhibits proliferation and invasion of human hepatocellular carcinoma cells via up-regulation of p53 and suppressing FoxM1. Acta Pharmacol Sin 2015; 36: 241-251 [PMID: 25557114 DOI: 10.1038/aps.2014.122] 10 Zhai B, Hu F, Jiang X, Xu J, Zhao D, Liu B, Pan S, Dong X, Tan G, Wei Z, Qiao H, Jiang H, Sun X. Inhibition of Akt reverses the acquired resistance to sorafenib by switching protective autophagy to autophagic cell death in hepatocellular carcinoma. Mol Cancer Ther 2014; 13: 1589-1598 [PMID: 24705351 DOI: 10.1158/1535-7163.MCT-13-1043] 11 Tovar V, Cornella H, Moeini A, Vidal S, Hoshida Y, Sia D, Peix J, Cabellos L, Alsinet C, Torrecilla S, Martinez-Quetglas I, Lozano JJ, Desbois-Mouthon C, Solé M, Domingo-Domenech J, Villanueva A, Llovet JM. Tumour initiating cells and IGF/FGF signalling contribute to sorafenib resistance in hepatocellular carcinoma. Gut 2017; 66: 530-540 [PMID: 26658144 DOI: 10.1136/gutjnl-2015-309501] 12 Rodríguez-Hernández MA, Chapresto-Garzón R, Cadenas M, Navarro-Villarán E, Negrete M, Gómez-Bravo MA, Victor VM, Padillo FJ, Muntané J. Differential effectiveness of tyrosine kinase inhibitors in 2D/3D culture according to cell differentiation, p53 status and mitochondrial respiration in liver cancer cells. Cell Death Dis 2020; 11: 339 [PMID: 32382022 DOI: 10.1038/s41419-020-2558-1] 13 Chen X, Lingala S, Khoobyari S, Nolta J, Zern MA, Wu J. Epithelial mesenchymal transition and hedgehog signaling activation are associated with chemoresistance and invasion of hepatoma subpopulations. J Hepatol 2011; 55: 838-845 [PMID: 21334406 DOI: 10.1016/j.jhep.2010.12.043] 14 Tang W, Chen Z, Zhang W, Cheng Y, Zhang B, Wu F, Wang Q, Wang S, Rong D, Reiter FP, De Toni EN, Wang X. The mechanisms of sorafenib resistance in hepatocellular carcinoma: theoretical basis and therapeutic aspects. Signal Transduct Target Ther 2020; 5: 87 [PMID: 32532960 DOI: 10.1038/s41392-020-0187-x] 15 Wong CM, Tsang FH, Ng IO. Non-coding RNAs in hepatocellular carcinoma: molecular functions and pathological implications. Nat Rev Gastroenterol Hepatol 2018; 15: 137-151 [PMID: 29317776 DOI: 10.1038/nrgastro.2017.169] 16 Wang S, Yang Y, Sun L, Qiao G, Song Y, Liu B. Exosomal MicroRNAs as Liquid Biopsy Biomarkers in Hepatocellular Carcinoma. Onco Targets Ther 2020; 13: 2021-2030 [PMID: 32210570 DOI: 10.2147/OTT.S232453] 17 Manganelli M, Grossi I, Ferracin M, Guerriero P, Negrini M, Ghidini M, Senti C, Ratti M, Pizzo C, Passalacqua R, Molfino S, Baiocchi G, Portolani N, Marchina E, De Petro G, Salvi A. Longitudinal Circulating Levels of miR-23b-3p, miR-126-3p and lncRNA GAS5 in HCC Patients Treated with Sorafenib. Biomedicines 2021; 9 [PMID: 34356875 DOI: 10.3390/biomedicines9070813] 18 de la Cruz-Ojeda P, Schmid T, Boix L, Moreno M, Sapena V, Praena-Fernández JM, Castell FJ, Falcón-Pérez JM, Reig M, Brüne B, GómezBravo MA, Giráldez Á, Bruix J, Ferrer MT, Muntané J. miR-200c-3p, miR-222-5p, and miR-512-3p Constitute a Biomarker Signature of Sorafenib Effectiveness in Advanced Hepatocellular Carcinoma. Cells 2022; 11 [PMID: 36078082 DOI: 10.3390/cells11172673] 19 Wang KC, Chang HY. Molecular mechanisms of long noncoding RNAs. Mol Cell 2011; 43: 904-914 [PMID: 21925379 DOI: 10.1016/j.molcel.2011.08.018] 20 Chowdhary A, Satagopam V, Schneider R. Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer. Front Genet 2021; 12: 649619 [PMID: 34276764 DOI: 10.3389/fgene.2021.649619] 21 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550 [PMID: 25516281 DOI: 10.1186/s13059-014-0550-8] 22 Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 2009; 4: 1184-1191 [PMID: 19617889 DOI: 10.1038/nprot.2009.97] 23 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102: 15545-15550 [PMID: 16199517 DOI: 10.1073/pnas.0506580102] 24 Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC. PGC-1alpharesponsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003; 34: 267-273 [PMID: 12808457 DOI: 10.1038/ng1180] 25 Fallmann J, Videm P, Bagnacani A, Batut B, Doyle MA, Klingstrom T, Eggenhofer F, Stadler PF, Backofen R, Grüning B. The RNA workbench 2.0: next generation RNA data analysis. Nucleic Acids Res 2019; 47: W511-W515 [PMID: 31073612 DOI: 10.1093/nar/gkz353] 26 Mackowiak SD. Identification of novel and known miRNAs in deep-sequencing data with miRDeep2. Curr Protoc Bioinformatics 2011; Chapter 12: 12.10.1-12.10.15 [PMID: 22161567 DOI: 10.1002/0471250953.bi1210s36] 27 Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4: 44-57 [PMID: 19131956 DOI: 10.1038/nprot.2008.211] 28 Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, von Mering C. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021; 49: D605-D612 [PMID: 33237311 DOI: 10.1093/nar/gkaa1074] 29 Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017; 45: W98-W102 [PMID: 28407145 DOI: 10.1093/nar/gkx247] 30 Karagkouni D, Paraskevopoulou MD, Tastsoglou S, Skoufos G, Karavangeli A, Pierros V, Zacharopoulou E, Hatzigeorgiou AG. DIANALncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts. Nucleic Acids Res 2020; 48: D101-D110 [PMID: 31732741 DOI: 10.1093/nar/gkz1036] 31 Paraskevopoulou MD, Karagkouni D, Vlachos IS, Tastsoglou S, Hatzigeorgiou AG. microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions. Nat Commun 2018; 9: 3601 [PMID: 30190538 DOI: 10.1038/s41467-018-06046-y] de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 18 January 21, 2025 Volume 31 Issue 3 32 Fukunaga T, Iwakiri J, Ono Y, Hamada M. LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With TissueSpecific Expression and Subcellular Localization Data. Front Genet 2019; 10: 462 [PMID: 31191601 DOI: 10.3389/fgene.2019.00462] 33 Lang B, Armaos A, Tartaglia GG. RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data. Nucleic Acids Res 2019; 47: D601-D606 [PMID: 30445601 DOI: 10.1093/nar/gky967] 34 Villanueva A. Hepatocellular Carcinoma. N Engl J Med 2019; 380: 1450-1462 [PMID: 30970190 DOI: 10.1056/NEJMra1713263] 35 He J, He J, Min L, He Y, Guan H, Wang J, Peng X. Extracellular vesicles transmitted miR-31-5p promotes sorafenib resistance by targeting MLH1 in renal cell carcinoma. Int J Cancer 2020; 146: 1052-1063 [PMID: 31259424 DOI: 10.1002/ijc.32543] 36 Fornari F, Pollutri D, Patrizi C, La Bella T, Marinelli S, Casadei Gardini A, Marisi G, Baron Toaldo M, Baglioni M, Salvatore V, Callegari E, Baldassarre M, Galassi M, Giovannini C, Cescon M, Ravaioli M, Negrini M, Bolondi L, Gramantieri L. In Hepatocellular Carcinoma miR-221 Modulates Sorafenib Resistance through Inhibition of Caspase-3-Mediated Apoptosis. Clin Cancer Res 2017; 23: 3953-3965 [PMID: 28096271 DOI: 10.1158/1078-0432.CCR-16-1464] 37 Gramantieri L, Pollutri D, Gagliardi M, Giovannini C, Quarta S, Ferracin M, Casadei-Gardini A, Callegari E, De Carolis S, Marinelli S, Benevento F, Vasuri F, Ravaioli M, Cescon M, Piscaglia F, Negrini M, Bolondi L, Fornari F. MiR-30e-3p Influences Tumor Phenotype through MDM2/TP53 Axis and Predicts Sorafenib Resistance in Hepatocellular Carcinoma. Cancer Res 2020; 80: 1720-1734 [PMID: 32015093 DOI: 10.1158/0008-5472.CAN-19-0472] 38 Kohno T, Morishita A, Iwama H, Fujita K, Tani J, Takuma K, Nakahara M, Oura K, Tadokoro T, Nomura T, Yoneyama H, Kato K, Okano K, Suzuki Y, Nishiyama A, Himoto T, Masaki T. Comprehensive analysis of circulating microRNAs as predictive biomarkers for sorafenib therapy outcome in hepatocellular carcinoma. Oncol Lett 2020; 20: 1727-1733 [PMID: 32724415 DOI: 10.3892/ol.2020.11696] 39 Chen S, Xia X. Long noncoding RNA NEAT1 suppresses sorafenib sensitivity of hepatocellular carcinoma cells via regulating miR-335-cMet. J Cell Physiol 2019; 234: 14999-15009 [PMID: 30937906 DOI: 10.1002/jcp.27567] 40 Chen YT, Xiang D, Zhao XY, Chu XY. Upregulation of lncRNA NIFK-AS1 in hepatocellular carcinoma by m(6)A methylation promotes disease progression and sorafenib resistance. Hum Cell 2021; 34: 1800-1811 [PMID: 34374933 DOI: 10.1007/s13577-021-00587-z] 41 Zhang PF, Wang F, Wu J, Wu Y, Huang W, Liu D, Huang XY, Zhang XM, Ke AW. LncRNA SNHG3 induces EMT and sorafenib resistance by modulating the miR-128/CD151 pathway in hepatocellular carcinoma. J Cell Physiol 2019; 234: 2788-2794 [PMID: 30132868 DOI: 10.1002/jcp.27095] 42 Li W, Dong X, He C, Tan G, Li Z, Zhai B, Feng J, Jiang X, Liu C, Jiang H, Sun X. LncRNA SNHG1 contributes to sorafenib resistance by activating the Akt pathway and is positively regulated by miR-21 in hepatocellular carcinoma cells. J Exp Clin Cancer Res 2019; 38: 183 [PMID: 31053148 DOI: 10.1186/s13046-019-1177-0] 43 Guo Z, Zhang J, Fan L, Liu J, Yu H, Li X, Sun G. Long Noncoding RNA (lncRNA) Small Nucleolar RNA Host Gene 16 (SNHG16) Predicts Poor Prognosis and Sorafenib Resistance in Hepatocellular Carcinoma. Med Sci Monit 2019; 25: 2079-2086 [PMID: 30893293 DOI: 10.12659/MSM.915541] 44 Qin M, Meng Y, Luo C, He S, Qin F, Yin Y, Huang J, Zhao H, Hu J, Deng Z, Qiu Y, Hu G, Pan H, Qin Z, Huang Z, Yi T. lncRNA PRR34AS1 promotes HCC development via modulating Wnt/β-catenin pathway by absorbing miR-296-5p and upregulating E2F2 and SOX12. Mol Ther Nucleic Acids 2021; 25: 37-52 [PMID: 34168917 DOI: 10.1016/j.omtn.2021.04.016] 45 Liu C, Tang L, Xu M, Lin Y, Shen J, Zhou L, Ho L, Lu J, Ai X. LncRNA RUSC1-AS1 contributes to the progression of hepatocellular carcinoma cells by modulating miR-340-5p/CREB1 axis. Am J Transl Res 2021; 13: 1022-1036 [PMID: 33841637] 46 Chan LK, Tsui YM, Ho DW, Ng IO. Cellular heterogeneity and plasticity in liver cancer. Semin Cancer Biol 2022; 82: 134-149 [PMID: 33647386 DOI: 10.1016/j.semcancer.2021.02.015] 47 Safri F, Nguyen R, Zerehpooshnesfchi S, George J, Qiao L. Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications. Cancer Gene Ther 2024; 31: 1105-1112 [PMID: 38499648 DOI: 10.1038/s41417-024-00764-w] 48 Lu J, Guo J, Liu J, Mao X, Xu K. Long Non-coding RNA MALAT1: A Key Player in Liver Diseases. Front Med (Lausanne) 2021; 8: 734643 [PMID: 35145971 DOI: 10.3389/fmed.2021.734643] 49 Fan L, Huang X, Chen J, Zhang K, Gu YH, Sun J, Cui SY. Long Noncoding RNA MALAT1 Contributes to Sorafenib Resistance by Targeting miR-140-5p/Aurora-A Signaling in Hepatocellular Carcinoma. Mol Cancer Ther 2020; 19: 1197-1209 [PMID: 32220970 DOI: 10.1158/1535-7163.MCT-19-0203] 50 Jones S. An overview of the basic helix-loop-helix proteins. Genome Biol 2004; 5: 226 [PMID: 15186484 DOI: 10.1186/gb-2004-5-6-226] 51 Potts MA, Mizutani S, Garnham AL, Li Wai Suen CSN, Kueh AJ, Tai L, Pal M, Strasser A, Herold MJ. Deletion of the transcriptional regulator TFAP4 accelerates c-MYC-driven lymphomagenesis. Cell Death Differ 2023; 30: 1447-1456 [PMID: 36894688 DOI: 10.1038/s41418-023-01145-w] 52 Yang Z, He J, Gao P, Niu Y, Zhang J, Wang L, Liu M, Wei X, Liu C, Zhang C, Wang W, Du J, Li H, Hu W, Sun G. miR-769-5p suppressed cell proliferation, migration and invasion by targeting TGFBR1 in non-small cell lung carcinoma. Oncotarget 2017; 8: 113558-113570 [PMID: 29371929 DOI: 10.18632/oncotarget.23060] 53 Ding L, Feng Y, Li L. Circ_0001955 promotes the progression of non-small cell lung cancer via miR-769-5p/EGFR axis. Cell Cycle 2022; 21: 2433-2443 [PMID: 35920610 DOI: 10.1080/15384101.2022.2100681] 54 Chen Q, Cao M, Ge H. Knockdown of MALAT1 Inhibits the Progression of Chronic Periodontitis via Targeting miR-769-5p/HIF3A Axis. Biomed Res Int 2021; 2021: 8899863 [PMID: 33604388 DOI: 10.1155/2021/8899863] 55 Yan X, Liu Z, Chen Y. Regulation of TGF-beta signaling by Smad7. Acta Biochim Biophys Sin (Shanghai) 2009; 41: 263-272 [PMID: 19352540 DOI: 10.1093/abbs/gmp018] 56 Ou Y, Dai X, Chen X, Chen Y, Wu S, Zhou Q, Yang C, Jiang H. Circ-AFAP1 promote clear cell renal cell carcinoma growth and angiogenesis by the Circ-AFAP1/miR-374b-3p/VEGFA signaling axis. Cell Death Discov 2022; 8: 68 [PMID: 35173146 DOI: 10.1038/s41420-022-00865-1] 57 Hopp AK, Hottiger MO. Uncovering the Invisible: Mono-ADP-ribosylation Moved into the Spotlight. Cells 2021; 10 [PMID: 33808662 DOI: 10.3390/cells10030680] 58 Su Y, Xie R, Xu Q. LncRNA THAP9-AS1 highly expressed in tissues of hepatocellular carcinoma and accelerates tumor cell proliferation. Clin Res Hepatol Gastroenterol 2022; 46: 102025 [PMID: 36170961 DOI: 10.1016/j.clinre.2022.102025] 59 Ji K, Zhang Q, Song W, Mao T, Guo J, Min C, Zhang C, Tian Z, Li X. LncRNA PVT1 Promotes Cell Proliferation, Invasion, and Migration and Inhibits Cell Apoptosis by Phosphorylating YAP. Can J Gastroenterol Hepatol 2022; 2022: 5332129 [PMID: 35664988 DOI: 10.1155/2022/5332129] Zhou C, Yi C, Yi Y, Qin W, Yan Y, Dong X, Zhang X, Huang Y, Zhang R, Wei J, Ali DW, Michalak M, Chen XZ, Tang J. LncRNA PVT1 60 de la Cruz-Ojeda P et al. lncRNA-miRNA-mRNA in Sorafenib-treated liver cancer WJG https://www.wjgnet.com 19 January 21, 2025 Volume 31 Issue 3 promotes gemcitabine resistance of pancreatic cancer via activating Wnt/β-catenin and autophagy pathway through modulating the miR-6195p/Pygo2 and miR-619-5p/ATG14 axes. Mol Cancer 2020; 19: 118 [PMID: 32727463 DOI: 10.1186/s12943-020-01237-y] 61 Xu H, Hu YW, Zhao JY, Hu XM, Li SF, Wang YC, Gao JJ, Sha YH, Kang CM, Lin L, Huang C, Zhao JJ, Zheng L, Wang Q. MicroRNA-1955p acts as an anti-oncogene by targeting PHF19 in hepatocellular carcinoma. Oncol Rep 2015; 34: 175-182 [PMID: 25955388 DOI: 10.3892/or.2015.3957] 62 Ribatti D. Immunosuppressive effects of vascular endothelial growth factor. Oncol Lett 2022; 24: 369 [PMID: 36238855 DOI: 10.3892/ol.2022.13489] 63 Katz JP, Perreault N, Goldstein BG, Actman L, McNally SR, Silberg DG, Furth EE, Kaestner KH. Loss of Klf4 in mice causes altered proliferation and differentiation and precancerous changes in the adult stomach. Gastroenterology 2005; 128: 935-945 [PMID: 15825076 DOI: 10.1053/j.gastro.2005.02.022] 64 Li Y, Yu S, Li L, Chen J, Quan M, Li Q, Gao Y. KLF4-mediated upregulation of CD9 and CD81 suppresses hepatocellular carcinoma development via JNK signaling. Cell Death Dis 2020; 11: 299 [PMID: 32350244 DOI: 10.1038/s41419-020-2479-z] 65 Su JL, Chen PS, Johansson G, Kuo ML. Function and regulation of let-7 family microRNAs. Microrna 2012; 1: 34-39 [PMID: 25048088 DOI: 10.2174/2211536611201010034] 66 Yu J, Cao Q, Mehra R, Laxman B, Yu J, Tomlins SA, Creighton CJ, Dhanasekaran SM, Shen R, Chen G, Morris DS, Marquez VE, Shah RB, Ghosh D, Varambally S, Chinnaiyan AM. Integrative genomics analysis reveals silencing of beta-adrenergic signaling by polycomb in prostate cancer. Cancer Cell 2007; 12: 419-431 [PMID: 17996646 DOI: 10.1016/j.ccr.2007.10.016] Published by Baishideng Publishing Group Inc 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA Telephone: +1-925-3991568 E-mail: [email protected] Help Desk: https://www.f6publishing.com/helpdesk https://www.wjgnet.com © 2025 Baishideng Publishing Group Inc. All rights reserved.