A 66 amino acid micro-peptide encoded by long non-coding RNA RP11-119F7.5 was identified in hepatocellular carcinoma : Journal of Bio-X Research

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A 66 amino acid micro-peptide encoded by long non-coding RNA RP11-119F7.5 was identified in hepatocellular carcinoma

He, Chengwen; Liu, Zhiyong; Pang, Yanan; Jia, Yin; Qin, Qin; Kong, Ruijiao; Zhang, Hui; Liu, Shanrong*,

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Journal of Bio-XResearch 5(4):p 163-170, December 2022. | DOI: 10.1097/JBR.0000000000000132

Abstract

Introduction

Primary liver cancer is one of the most common malignant tumors in the world. Hepatocellular carcinoma (HCC) is the main pathological type of primary liver cancer.[1,2] Despite the continuous development of technology and treatment strategies, the successful treatment of liver cancer remains challenging, with obstacles in early detection, metastasis, disease recurrence, and poor prognosis.[3–5] The overall survival rate for HCC patients is only 10% to 25% in most countries.[6,7] The progression of liver cancer is a multi-step process. The evolution from normal hepatocytes to HCC goes through many pathological stages and involves many molecular events.

Protein-coding genes account for 2% of the overall human genome sequence.[8] A genome, nevertheless, was transcribed principally into non-coding RNAs (ncRNAs), which including small nucleolar RNA, microRNAs, circular RNA and lncRNA.[9–11] It is worth noting that, with the extensive and in-depth studies on ncRNA in recent years, it has been found that ncRNA plays a very important role in the maintenance of genome stability, and could directly participate in the regulation of pathophysiological processes as a functional molecule. Long non-coding RNAs (lncRNAs) are mRNA-like transcripts over 200 nucleotides in length that lack protein-coding ability.[12,13] LncRNAs are involved in the regulation of many biological processes. Multiple studies have shown that lncRNAs are also involved in the occurrence and development of numerous diseases,[14–16] including cancer.[17–19] Notably, in recent years, some research groups have identified several biologically relevant micro-peptides encoded by small open reading frames (smORFs) in lncRNA transcripts from a variety of species.[20] In zebrafish, for example, lncRNA Loc100506013 encoded a micro-peptide called Toddler functions as an activator of APJ/Apelin receptor signaling and promotes gastrula formation.[21] In humans, the lncRNA LINC01116-encoded peptide contributes to neuronal function and disease.[22] Identification of translated smORFs remains technically challenging and therefore the mechanisms and modes of action of these micro-peptides are poorly understood.[23–25]

This study aimed to identify a novel micro-peptide translated by ncRNA RP11-119F7 and attempted to predict its role in HCC.

Materials and methods

Cell culture and treatments

HCC cell lines Hep3B (HB-8064) and Huh7 (JCRB0403), human prostate cancer cell line DU145 (HTB-81), human pancreatic cancer cell line SW1990 (CRL-2172), human colon cancer cell line HCT116 (CCL-247) were purchased from American Type Culture Collection (ATCC, Manassas, VA). Two human immortalized hepatic stellate cell lines (QSG7701, L-02) were purchased from Chinese Academy of Sciences Cell Bank (Shanghai, China). DU145 cells were cultured in RPMI 1640 medium (Gibco, Carlsbad, CA) containing 10% fetal bovine serum (FBS) and penicillin (100U/mL)/streptomycin (100μg/mL). HCT116 cells were cultured in McCoy’s 5A medium (Gibco, CA) containing 10% FBS and penicillin (100U/mL)/streptomycin (100µg/mL). The other cell lines were cultured in DMEM (Hyclone, Logan, UT) containing 10% FBS and penicillin (100U/mL)/streptomycin (100µg/mL). All cells were cultured in a 37°C humidified incubator with 5% CO2. The four cell lines (Hep3B, DU145, SW1990, and HCT116) were used to detect overlapping genes. Huh7 and Hep3 were used to screen candidate genes.

Human clinical samples

To compare overlap gene expression differences between cancer tissues and adjacent tissues in clinical HCC samples. We collected the samples of HCC tissue and paired para-cancer tissue (>2 cm from tumor margin) from HCC patients (n=6) undergoing tumor resection in Changhai Hospital, Naval Military Medical University. All samples were immediately frozen in liquid nitrogen before subsequent analysis. This study followed the principles established in the Declaration of Helsinki and was approved by the Changhai Internal Review and Ethics Boards at Naval Military Medical University (approval No. CHEC2020-081) on June 6, 2020. Informed consent was obtained from all patients.

Plasmid construction and cell transfection

To validate the translation potential of candidates, we constructed flag and GFP fusion sequences with smORF by polymerase chain reaction (PCR) amplification. The flag fusion sequences were subcloned into pcDNA3.1 and pSPT19 vectors (Invitrogen, Carlsbad, CA). The GFP fusion sequence was subcloned into PEGFP-N1 vector (Invitrogen). Lipofectamine 3000 (Invitrogen) was used to transfect the pcDNA3.1 and PEGFP-N1 subcloned plasmids into Huh7 HCC cells in accordance with the manufacturer’s protocol. Primer sequences are shown in Additional Table 1, https://links.lww.com/JR9/A42.

In vitro translation assay

The smORF-flag fusion subcloned pSPT19 vector was applied to explore the translation ability of smORFs in vitro. Four micrograms of subcloned vector was added in a clear tube and incubated 2 hours at 37°C, then put the tube on ice for 5 minutes to stop the reaction. The production was analyzed using SDS-PAGE electrophoresis.

RNA-immunoprecipitation and RIP-sequencing

Ribosomal protein S6 (RPS6) is a critical component of the 40S ribosomal subunit and interacts with the 5′-m7 GpppG cap-binding complex to regulate mRNA translation initiation.[26,27] To obtain the signature of ribosome-binding RNAs, we performed RNA-immunoprecipitation (RIP) assay using primary antibody for RPS6 (ab70227, Abcam, Cambridge, UK) and high-throughput sequencing in four cancer cell lines (Hep3B, DU145, HCT116, and SW1990) using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, Bedford, MA) following the manufacturer’s protocol. Each cell lines have input and anti-RPS6 samples for analysis. The cDNA libraries were generated by Biotechnology Company (Shanghai, China) using RNA. High-throughput RNA-seq was performed using an Illumina HiSeq 2500 sequencer (Illumina, San Diego, CA). RIP-sequencing (RIP-seq) reads were aligned to the human reference genome version 19 using the TopHat algorithm. For expression analysis, densities of genes were determined by the value of reads per kb per million reads. We analyzed the sequencing results sand explore the common and critical RNAs in four different tumor cells by Venn diagram analysis.

smORF identification

First, we obtain the full sequence of interested RNA from UCSC database (http://genome.ucsc.edu/). Then, the nucleotide sequence of RNA was input in ORF Finder tool of NCBI (https://www.ncbi.nlm.nih.gov/orffinder/) to identify the open reading frames (ORFs) for potential protein encoding segments.

Quantitative reverse transcription-PCR

Quantitative reverse transcription (qRT-PCR) was performed to analyze the expression levels of interested RNA. Total RNA was extracted from HCC cells and clinical tissues using TRIzol® reagent (Thermo Fisher Scientific, Waltham, MA). cDNA was synthesized using the Hifair® II 1st Strand cDNA Synthesis SuperMix kit (Yeasen Biotech, Shanghai, China). qPCR analysis was conducted using the Hieff® QPCR SYBR Green Master Mix kit (Yeasen Biotech) in QuantStudio™ (Thermo Fisher Scientific). qPCR were performed as following thermocycling conditions: 95°C for 5 minutes, followed by 40 cycles at 95°C for 10 seconds, 60°C for 30 seconds and elongation at 72°C for 2 minutes. Relative expression was determined using the comparative 2−ΔCq method. β-actin was used as an endogenous control. Primer sequences are shown in Additional Table 1, https://links.lww.com/JR9/A42.

Western blot assay

To determine the translation ability of smORFs in vivo, we explored the flag and GFP protein expression of recombinant plasmids using western blotting. The transfected Huh7 cells were lysed with RIPA buffer (Beyotime Biotechnology, Shanghai, China) containing protease inhibitor cocktail (MCE, NJ). The bicinchoninic acid method (Beyotime Biotechnology) was used to determine the protein concentration of cell lysates. Equal amounts of protein (30µg) were separated by 10% SDS-PAGE and transferred onto 0.45-µm PVDF membranes (Bio-Rad Laboratories, CA). The membranes were blocked using 5% non-fat milk solution at room temperature for 1 hour and incubated with primary antibodies at 4°C overnight. The following primary antibodies were used: flag (1:1000 dilution, AE063, ABclonal, Wuhan, Hubei Province, China), GFP (1:1000 dilution, AE011, ABclonal), GAPDH (1:1000 dilution, A19056, ABclonal). The membranes were then incubated with horseradish peroxidase–conjugated goat anti-rabbit IgG (H + L) (1:10,000 dilution, AS014, ABclonal) secondary antibody at room temperature for 1 hour. Protein bands were visualized using Super ECL Detection Reagent (Yeasen Biotech) in ImageQuant LAS4000 (GE Healthcare). Protein levels were normalized to that of GAPDH.

Immunofluorescence

We performed immunofluorescence assay to further confirm the smORF translation by detecting flag expression in HCC cells. Cells transfected with recombinant plasmids were fixed with 4% formaldehyde for 20 minutes and permeated with 0.3% Triton X-100 (Bio-Light, Zhuhai, Guangdong Province, China) for 10 minutes. After three washes in PBS, the cells were blocked with 10% goat serum for 1 hour and then incubated overnight with flag (1:200 dilution, AE063, ABclonal) at 4°C. After washing, cells were incubated with Alexa 488–conjugated goat anti-rabbit IgG (Yeasen Biotech) at room temperature for 1 hour. Next, 4′,6-diamino-2-phenylindole (Yeasen Biotech) was applied to stain the nuclei. Fluorescence was observed with a fluorescence microscope (Olympus, Tokyo, Japan).

RNA fluorescence in situ hybridization

fluorescence in situ hybridization (FISH) assay were conducted to determine the cellular location of interested RNA. Wild-type HCC cells were seeded on 24-well glass coverslips. When they reached a density of 60% to 70%, the cells were washed with PBS twice, fixed with 4% paraformaldehyde for 15 minutes and permeated with cold PBS containing 0.5% Triton X-100. The cells were washed with wash buffer and incubated with pre-hybridization buffer at 37°C for 30 minutes. Linear RNA probes were mixed with hybridization buffer at a concentration of 500 nM and the samples were added to cells and incubated overnight at 37°C. After 12 hours, washing cells twice using 4× SSC contain 0.1% Tween-20 at 42°C for 5 minutes, and then washing cells twice in 2× SSC and 1× SSC, respectively. Cells were incubated with 4′,6-diamino-2-phenylindole for 10 minutes. Cells were analyzed using a fluorescence microscope. Anti-sense and sense oligonucleotides for the RP11-119F7.5, 18S and U6 probes were designed by RIBO Biotechnology Co., Ltd. (Guangzhou, China).

Co-immunoprecipitation and mass spectrometry

To further analysis the proteins interacted with smORF encoded peptide, co-immunoprecipitation (Co-IP) and mass spectrometry (MS; OEbiotech, Shanghai, China) were conducted. Huh7 cells (5×106) transfected with smORF plasmids were lysed with protein lysis buffer. Co-IP was performed as previously reported[20] using anti-Flag antibody (Cell Signaling Technology, Danvers, MA), the Dynabeads Protein G Immunoprecipitation Kit (Thermo Fisher Scientific) and a silver staining kit (Beyotime Biotechnology) following the manufacturers’ protocols. The protein bands of interest were cut out of the gel and analyzed by MS. The identified potential proteins interacted with smORF encoded peptide were further analyzed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db.org/). And the protein–protein interaction network was constructed using Cytoscape software (version 3.4.0; National Resource for Network Biology). To explore the biological functions and signaling pathways associated with the identified proteins, we further performed Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov).

Statistical analysis

Statistical analyses were performed using GraphPad Prism 5.0 (GraphPad Software, San Diego, CA, www.graphpad.com). Data that conformed to a normal distribution were expressed as the mean±standard deviation (SD). Comparisons between parametric data were analyzed by two-tailed Student t-tests. Comparisons between non-parametric data were analyzed by Mann–Whitney U tests. P<0.05 was considered as statistically significant.

Results

Identification of ribosome-binding genes

Overlapping genes were two or more structural genes that shared an identical gene sequence and were widely present in the genome of organisms.[28] Overlapping genes not only enable organisms to use limited genetic information to encode more proteins, but also participate in gene expression regulation.[29] A total of 223 overlapping genes were captured by RPS6-RIP (Fig. 1A). Venn diagram analysis revealed that 60 overlapping genes were detected in four cancer cell lines (Fig. 1B). Among the identified genes, 19 overlapping RNA lacking parental genes were selected (Fig. 1C). qRT-PCR showed that six of the candidate genes (RP11-298J20.4, RP11-4O1.2, RP11-119F7.5, RP11-448G15.3, HCP5, RP11-517B11.7) were expressed in Huh7 and Hep3B cells (Fig. 1D). We performed further analysis of these six candidate genes and found that five (RP11-298J20.4, RP11-4O1.2, RP11-119F7.5, RP11-448G15.3, RP11-517B11.7) displayed higher expression levels in HCC cell lines (Huh7, Hep3B) and tumor tissues than in liver cell lines (L-02, QSG-7701) and para-tumor tissues, respectively (Fig. 1E, F). We performed additional RIP assays and confirmed that four of the genes (RP11-4O1.2, RP11-119F7.5, RP11-448G15.3, RP11-517B11.7) bound RPS6 (Fig. 1G).

F1
Figure 1.:
Identification of ribosome-binding genes. (A) RNA-immunoprecipitation followed by high-throughput sequencing was used to identify ribosomal-binding RNA molecules in four cancer cell lines (Hep3B, HCT116, SW1990, DU145). A total of 223 overlapping genes were captured by RPS6-RIP. (B) Venn diagram analysis identified 60 overlapping genes binding ribosomes in the four cell lines (HCT116, SW1990, DU145, Hep3B). (C) The expression levels of 19 unknown sequences without parental genes were detected in Huh7 and Hep3B cells. (D) Six candidate genes (P3 RP11-298J20.4, P10 RP11-4O1.2, P11 RP11-119F7.5, P33 RP11-448G15.3, P37 HCP5, P42 RP11-517B11.7) were expressed in HCC cell lines (Huh7 and Hep3B). (E) The expressions of the five candidate genes (P3 RP11-298J20.4, P10 RP11-4O1.2, P11 RP11-119F7.5, P33 RP11-448G15.3, P42 RP11-517B11.7) were higher in HCC cell lines (Huh7 and Hep3B) than in normal liver cell lines (QSG7701, L02) (*P<0.05). (F) The expression levels of the five genes (P3 RP11-298J20.4, P10 RP11-4O1.2, P11 RP11-119F7.5, P33 RP11-448G15.3, P42 RP11-517B11.7) were significantly higher in six pairs of tumor tissues of HCC patients compared with levels in para-tumor control tissues (*P<0.05). (G) RIP assays and qRT-PCR with specific primers of the four candidate genes (P10 RP11-4O1.2, P11 RP11-119F7.5, P33 RP11-448G15.3, P42 RP11-517B11.7) verified that the four genes bound ribosomes (*P<0.05). IgG=negative control, input=positive control, RPS6=target gene. HCC=hepatocellular carcinoma, qRT-PCR=quantitative reverse transcription-polymerase chain reaction, RPS6-RIP=ribosomal protein S6-RNA-immunoprecipitation.

RP11-119F7.5 encodes a micro-peptide

We obtained the full length of the four gene sequences from the UCSC database and analyzed the open reading frames by ORF Finder; the conditions were set as an ATG start and ORF length of less than 300 bp. All four genes contain smORFs in their RNA sequences that potentially encode putative micro-peptides of less than 100 amino acids (Fig. 2A). To determine the translation potential of the four candidate smORFs, we subcloned a FLAG epitope tag into the C-terminal of the four selected smORFs before the stop codon, and the fusion sequences were then cloned into three different plasmid vectors (pSPT19, pcDNA3.1, and PEGFP-N1) (Fig. 2B). We performed coupled transcription and translation reactions and found that the pSPT19 plasmids encoded small peptides in vitro (Fig. 2C). We then transfected the pcDNA3.1 constructs into Huh7 cells, and a single 7.2 kDa micro-peptide was encoded from the candidate smORF of RP11.119F7.5 (Fig. 2D). We transfected the recombinant pEGFP-N1 plasmids with smORFs in HCC cells, and western blot analysis revealed a band above GFP in the RP11.119F7.5 recombinant plasmid lane (Fig. 2E). The coding potential of the RP11-119F7.5 vector was also confirmed by immunofluorescence assay (Fig. 2F). These results suggested that RP11.119F7.5 contains a smORF and encodes a small peptide in HCC cells. FISH assay revealed that RP11-119F7.5 was localized in the cytoplasm and nucleoplasm of HCC cells (Fig. 2G).

F2
Figure 2.:
Non-coding RNA RP11-119F7.5 encodes a 66 amino acid micro-peptide. (A) Sequence information of the four candidate genes. (B) The full-length smORF sequences with a FLAG tag were cloned into pSPT19, pcDNA3.1, and pEGFP-N1 plasmids. (C) In vitro translation reaction of the PSPT19 plasmids containing the four candidate genes followed by western blot analysis. (D) Huh7 cells were transfected with pcDNA3.1 plasmids containing the four candidate genes, followed by western blot analysis. A single 7.2 kDa peptide was produced from the target smORF of RP11.119F7.5. (E) Huh7 cells were transfected with recombinant pEGFP-N1 plasmid containing the smORFs, and western blot results revealed a band migrating above GFP in the RP11.119F7.5 recombinant plasmid lane. (F) Immunofluorescence assay in Huh7 cells transfected with the plasmid expressing RP11-119F7.5. Green, FLAG; blue, DAPI (scale bar=20 µm). (G) RNA fluorescence in situ hybridization assays showed that RP11-119F7.5 was localized in the cytoplasm and nucleoplasm in Huh7 cells. DAPI=4′,6-diamino-2-phenylindole, ORF=open reading frames, smORF=small open reading frames.

Identification of proteins potentially binding to the micro-peptide

To explore the molecular mechanism of the small peptide, we performed Co-IP using FLAG antibody in HCC cells transfected with the FLAG-tagged construct, and candidate proteins that potentially interact with the small peptide were identified by silver staining and MS (Fig. 3A). A protein–protein interaction network of the identified proteins was determined (Fig. 3B). GO enrichment analysis showed that the micro-peptide–interacting proteins were mainly involved in extracellular exosomes, indicating the small micro-peptide modulates the tumor development through exosomes secretion. The identified proteins were also involved in several biological functions such as protein binding, poly(A) RNA binding, translational initiation, and nuclear-transcribed mRNA catabolic process (Fig. 3C). KEGG pathway analysis revealed that the identified proteins were enriched in pathways including ribosome, biosynthesis of amino acids, carbon metabolism, biosynthesis of antibiotics, glycolysis and gluconeogenesis, pathogenic Escherichia coli infection, and influenza A (Fig. 3D).

F3
Figure 3.:
Identification of proteins that bind with the micro-peptide encoded by RP11-119F7.5 (A) Co-immunoprecipitation and mass spectrometry were used to identify proteins that interact with the peptide. (B) Protein–protein interaction network of the peptide-interacting proteins. (C) GO enrichment analysis showed that the peptide-interacting proteins were mainly involved in the extracellular exosome. (D) KEGG pathways indicated that the identified proteins were enriched in pathways including ribosome, biosynthesis of amino acids, carbon metabolism, biosynthesis of antibiotics, glycolysis and gluconeogenesis, pathogenic Escherichia coli infection and influenza A. GO=Gene ontology, KEGG=Kyoto Encyclopedia of Genes and Genomes.

Discussion

In this study, we examined possible polyadenylated, ribosomal-associated lncRNAs in different cancer cell lines. We found that the lncRNA RP11-119F7.5 is upregulated in HCC tissues compared with that in para-cancer tissues. Furthermore, we identified a 66 amino acid micro-peptide encoded by RP11-119F7.5 in HCC cells. MS data revealed that various proteins bind the micro-peptide encoded by RP11-119F7.5.

Many ncRNAs play an important role in the occurrence and development of liver cancer.[30–32] These ncRNAs function by a variety of molecular mechanisms, such as regulating RNA–DNA–protein interactions.[33,34] Some ncRNAs localize to specific regions of the nucleus and play transcriptional regulation functions.[35,36] Other ncRNAs function as competitive endogenous RNA molecules to regulate miRNAs.[37]Previous studies showed that several lncRNAs bind to ribosomes and have coding potential in cancer cells.[38,39] In this study, we identified ribosome-associated lncRNAs in cancer cell lines by RIP-seq using RPS6 antibody. We identified a novel polypeptide produced by the translation of RP11-119F7.5. Recent studies have found that there are millions of smORF sequences in eukaryotic genomes that have the ability to encode and translate proteins (<100 codons).[40,41]These ncRNAs encode functional peptides that are important in regulating many physiological and pathological processes.[42] Some reports have indicated that the functional peptides encoded by ncRNAs may play an important role in the development of tumors.[43] In our study, we found that RP11-119F7.5 was highly expressed in HCC tissues compared with that in the corresponding para-cancer tissues. At the same time, GO enrichment analysis showed that the micro-peptide–interacting proteins were mainly located in extracellular exosome, past research have shown that in the process of tumorigenesis and development, exosomal ncRNA plays a role in changing the tumor microenvironment, mediating the proliferation, metastasis, and drug resistance of tumor cells, promoting angiogenesis and mediating hypoxia signals. So it is reasonable to speculate that the micro-peptide encoded by ncRNA might regulate the tumor physiological status in exosomes. We also found that these identified proteins were involved in several biological functions like protein binding, poly(A) RNA binding, translational initiation, and the nuclear-transcribed mRNA catabolic process. KEGG pathway analysis revealed that the enriched pathways of the identified proteins included ribosome, biosynthesis of amino acids, carbon metabolism, biosynthesis of antibiotics, glycolysis and gluconeogenesis, pathogenic Escherichia coli infection, and influenza A. Our results identified various proteins that bind to the micro-peptide encoded by RP11-119F7.5 that may be involved in HCC. These findings suggest that micro-peptides encoded by lncRNAs are not redundant products of the translation process but may be involved in the occurrence and development of HCC.

There are some limitations to our study. First, the exact role of the micro-peptides in the development and progression of HCC was not identified. Second, our current work has not examined mechanisms of the micro-peptide in HCC.

In conclusion, our study found that a 66 amino acid micro-peptide encoded by ncRNA RP11-119F7.5 may be involved in the development of HCC. Future studies on the pathological functions of functional peptides or proteins produced by the translation of ncRNAs may open a new door for research in the cancer field.

Acknowledgments

None.

Author contributions

SL conceived the project and designed the experiments; ZL, YP, YJ, QQ, RK, and HZ performed the experiments and analyzed data. CH designed and wrote the manuscript. SL revised the manuscript. All authors approved the final manuscript for publication.

Financial support

This work was supported by the State Key Program of National Natural Science Foundation of China (No. 82030073). The funder did not participate in data collection and analysis, manuscript writing or submission.

Institutional review board statement

This study involving human tissue specimens was conducted in accordance with Declaration of Helsinki and approved by the Institutional Review Board of Changhai Hospital, Naval Military Medical University, China (approval No. CHEC2020-081) on June 6, 2020.

Conflicts of interest

The authors declare that there are no conflicts of interest.

References

[1]. Fornari F, Giovannini C, Piscaglia F, et al. Elucidating the molecular basis of sorafenib resistance in HCC: current findings and future directions. J Hepatocell Carcinoma 2021;8:741–757.
[2]. Galun D, Bogdanovic A, Zivanovic M, et al. Short- and long-term outcomes after hepatectomy in elderly patients with hepatocellular carcinoma: an analysis of 229 cases from a developing country. J Hepatocell Carcinoma 2021;8:155–165.
[3]. Zhang H, Qiu C, Zeng H, et al. Upregulation of stress-induced protein kinase CK1 Delta is associated with a poor prognosis for patients with hepatocellular carcinoma. Genet Test Mol Biomarkers 2021;25:504–514.
[4]. Orcutt ST, Anaya DA. Liver resection and surgical strategies for management of primary liver cancer. Cancer Control 2018;25:1073274817744621.
[5]. Li Q, Xiong DL, Wang H, et al. High expression of SLC41A3 correlates with poor prognosis in hepatocellular carcinoma. Onco Targets Ther 2021;14:2975–2988.
[6]. Suresh M, Menne S. Application of the woodchuck animal model for the treatment of hepatitis B virus-induced liver cancer. World J Gastrointest Oncol 2021;13:509–535.
[7]. Bai Y, Pei W, Zhang X, et al. ApoM is an important potential protective factor in the pathogenesis of primary liver cancer. J Cancer 2021;12:4661–4671.
[8]. Srivastava A, Giangiobbe S, Skopelitou D, et al. Whole genome sequencing prioritizes CHEK2, EWSR1, and TIAM1 as possible predisposition genes for familial non-medullary thyroid cancer. Front Endocrinol (Lausanne) 2021;12:600682.
[9]. Huang XC, Pang FX, Ou SS, et al. Risk score based on two microRNAs as a prognostic marker of hepatocellular carcinoma and the corresponding competitive endogenous RNA network. Int J Gen Med 2021;14:3377–3385.
[10]. Rong D, Sun G, Wu F, et al. Epigenetics: roles and therapeutic implications of non-coding RNA modifications in human cancers. Mol Ther Nucleic Acids 2021;25:67–82.
[11]. Lin YH, Wu MH, Liu YC, et al. LINC01348 suppresses hepatocellular carcinoma metastasis through inhibition of SF3B3-mediated EZH2 pre-mRNA splicing. Oncogene 2021;40:4675–4685.
[12]. Chi Y, Wang D, Wang J, et al. Long non-coding RNA in the pathogenesis of cancers. Cells 2019;8:1015.
[13]. Zhao S, Guan B, Mi Y, et al. LncRNA MIR17HG promotes colorectal cancer liver metastasis by mediating a glycolysis-associated positive feedback circuit. Oncogene 2021;40:4709–4724.
[14]. Gao F, He S, Jin A. MiRNAs and lncRNAs in NK cell biology and NK/T-cell lymphoma. Genes Dis 2020;8:590–602.
[15]. Fu W, Zhao J, Hu W, et al. LINC01224/ZNF91 promote stem cell-like properties and drive radioresistance in non-small cell lung cancer. Cancer Manag Res 2021;13:5671–5681.
[16]. Song HK, Kim SY. The role of sex-specific long non-coding RNAs in cancer prevention and therapy. J Cancer Prev 2021;26:98–109.
[17]. Peng PH, Lai JC, Chang JS, et al. Induction of epithelial-mesenchymal transition (EMT) by hypoxia-induced lncRNA RP11-367G18.1 through regulating the histone 4 lysine 16 acetylation (H4K16Ac) mark. Am J Cancer Res 2021;11:2618–2636.
[18]. Chang L, Li J, Ding J, et al. Roles of long noncoding RNAs on tumor immune escape by regulating immune cells differentiation and function. Am J Cancer Res 2021;11:2369–2385.
[19]. Wang X, Yang P, Zhang D, et al. LncRNA SNHG14 promotes cell proliferation and invasion in colorectal cancer through modulating miR-519b-3p/DDX5 axis. J Cancer 2021;12:4958–4970.
[20]. Pang Y, Liu Z, Han H, et al. Peptide SMIM30 promotes HCC development by inducing SRC/YES1 membrane anchoring and MAPK pathway activation. J Hepatol 2020;73:1155–1169.
[21]. Pauli A, Norris ML, Valen E, et al. Toddler: an embryonic signal that promotes cell movement via Apelin receptors. Science 2014;343:1248636.
[22]. Guo ZW, Meng Y, Zhai XM, et al. Translated long non-coding ribonucleic acid ZFAS1 promotes cancer cell migration by elevating reactive oxygen species production in hepatocellular carcinoma. Front Genet 2019;10:1111.
[23]. Guerra-Almeida D, Tschoeke DA, Nunes-da-Fonseca R. Understanding small ORF diversity through a comprehensive transcription feature classification. DNA Res 2021;28:dsab007.
[24]. Zhang Q, Wu E, Tang Y, et al. Deeply mining a universe of peptides encoded by long noncoding RNAs. Mol Cell Proteomics 2021;20:100109.
[25]. Douka K, Birds I, Wang D, et al. Cytoplasmic long noncoding RNAs are differentially regulated and translated during human neuronal differentiation. RNA 2021;27:1082–1101.
[26]. Shirakawa Y, Hide T, Yamaoka M, et al. Ribosomal protein S6 promotes stem-like characters in glioma cells. Cancer Sci 2020;111:2041–2051.
[27]. Abdelmeguid NE, Khalil MI, Badr NS, et al. Ameliorative effects of colostrum against DMBA hepatotoxicity in rats. Saudi J Biol Sci 2021;28:2254–2266.
[28]. Wright BW, Molloy MP, Jaschke PR. Overlapping genes in natural and engineered genomes. Nat Rev Genet 2022;23:154–168.
[29]. Chen CH, Pan CY, Lin WC. Overlapping protein-coding genes in human genome and their coincidental expression in tissues. Sci Rep 2019;9:13377.
[30]. Sukowati CHC, Cabral LKD, Tiribelli C, et al. Circulating long and circular noncoding RNA as non-invasive diagnostic tools of hepatocellular carcinoma. Biomedicines 2021;9:90.
[31]. Niu ZS, Wang WH, Dong XN, et al. Role of long noncoding RNA-mediated competing endogenous RNA regulatory network in hepatocellular carcinoma. World J Gastroenterol 2020;26:4240–4260.
[32]. Gupta M, Chandan K, Sarwat M. Role of microRNA and long non-coding RNA in hepatocellular carcinoma. Curr Pharm Des 2020;26:415–428.
[33]. Kwok ZH, Ni K, Jin Y. Extracellular vesicle associated non-coding RNAs in lung infections and injury. Cells 2021;10:965.
[34]. Liu X, Feng S, Zhang XD, et al. Non-coding RNAs, metabolic stress and adaptive mechanisms in cancer. Cancer Lett 2020;491:60–69.
[35]. Ramya Devi KT, Karthik D, Mahendran T, et al. Long noncoding RNAs: role and contribution in pancreatic cancer. Transcription 2021;12:12–27.
[36]. Rosace D, López J, Blanco S. Emerging roles of novel small non-coding regulatory RNAs in immunity and cancer. RNA Biol 2020;17:1196–1213.
[37]. Chang C, Kong W, Mou X, et al. Investigating the correlation between DNA methylation and immune-associated genes of lung adenocarcinoma based on a competing endogenous RNA network. Mol Med Rep 2020;22:3173–3182.
[38]. Rodrigues DC, Mufteev M, Weatheritt RJ, et al. Shifts in ribosome engagement impact key gene sets in neurodevelopment and ubiquitination in Rett syndrome. Cell Rep 2020;30:4179–4196.e11.
[39]. Yu MJ, Zhao N, Shen H, et al. Long noncoding RNA MRPL39 inhibits gastric cancer proliferation and progression by directly targeting miR-130. Genet Test Mol Biomarkers 2018;22:656–663.
[40]. Immarigeon C, Frei Y, Delbare SYN, et al. Identification of a micropeptide and multiple secondary cell genes that modulate Drosophila male reproductive success. Proc Natl Acad Sci USA 2021;118:e2001897118.
[41]. Yeasmin F, Imamachi N, Tanu T, et al. Identification and analysis of short open reading frames (sORFs) in the initially annotated noncoding RNA LINC00493 from human cells. J Biochem 2021;169:421–434.
[42]. Chen Y, Ho L, Tergaonkar V. sORF-Encoded MicroPeptides: new players in inflammation, metabolism, and precision medicine. Cancer Lett 2021;500:263–270.
[43]. Zhou B, Yang H, Yang C, et al. Translation of noncoding RNAs and cancer. Cancer Lett 2021;497:89–99.
Keywords:

carcinoma; hepatocellular; micro-peptide; ncRNAs; translation; identification

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