Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients

The Immune-related ceRNA Network in Prognosis of Cervical Cancer

BACKGROUND

Immunotherapy is gaining attention and it is being included as one of the treatment strategies for cancer patients. However, the molecular mechanisms of immune-related genes and their affinity for cervical cancer progression remain unclear. In this study, we have developed an immune-related competing endogenous RNA [ceRNA] network and assessed the tumour infiltrating immune cells towards the prognosis of cervical cancer.

METHODS

Differential RNA expression pattern between stages I and II-IV of cervical cancer patients from The Cancer Genome Atlas [TCGA] was analyzed. Immune-related ceRNA network based on the immune gene signatures were retrieved and their targets were predicted using miRwalk 3.0. CIBERSORT was employed to identify the immune cell types based on their respective transcripts. The prognostic significance of RNAs in the ceRNA network and immune cell subsets was analyzed.

RESULTS

Significant differences in 22 long non-coding RNAs [lncRNAs], 15 microRNAs [miRNAs], and 252 messenger RNAs [mRNAs] between stages I and II-IV of cervical cancer were observed. Further, we shortlisted the 49 immune-related mRNAs based on immune gene signature and predicted their target miRNAs and lncRNAs. A potential ceRNA network of 4 lncRNAs, 10 miRNAs, and 11 mRNAs had a strong correlation for prognosis. Out of 11 protein-coding immune mRNAs, IRF4 and AZGP1 had high degrees of interaction. In addition, the evaluation of immune cell subsets showed increased infiltration of M1 macrophages had better survival outcome.

CONCLUSIONS

We have identified an immune-related ceRNA network based on differentially expressed transcripts between stages I and II-IV which may help predict the prognosis of cervical cancer.

CDC20 May Serve as a Potential Biomarker-Based Risk Score System in Predicting the Prognosis of Patients with Hepatocellular Carcinoma

Background

The specificity and sensitivity of hepatocellular carcinoma (HCC) diagnostic markers are limited, hindering the early diagnosis and treatment of HCC patients. Therefore, improving prognostic biomarkers for patients with HCC is urgently needed.

Methods

HCC-related datasets were downloaded from the public databases. Differentially expressed genes (DEGs) between HCC and adjacent nontumor liver tissues were then identified. Moreover, the intersection of DEGs in four datasets (GSE138178, GSE77509, GSE84006, and TCGA) was used in the functional enrichment, and module genes were obtained by a coexpression network. Cox and Kaplan-Meier analyses were used to identify overall survival- (OS-) related genes from module genes. Area under the curve (AUC) > 0.9 of OS-related genes was then carried out in order to perform the protein-protein interaction network. The feature genes were identified by least absolute shrinkage and selection operator (LASSO). Furthermore, the hub gene was identified through the univariate Cox model, after which the correlation analysis between the hub gene and pathways was explored. Finally, infiltration in immune cell types in HCC was analyzed.

Results

A total of 2,227 upregulated genes and 1,501 downregulated DEGs were obtained in all four datasets, which were mainly found to be involved in the cell cycle and retinol metabolism. Accordingly, 998 OS-related genes were screened to construct the LASSO model. Finally, 8 feature genes (BUB1, CCNB1, CCNB2, CCNA2, AURKB, CDC20, OIP5, and TTK) were obtained. CDC20 was shown to serve as a poor prognostic gene in HCC and was mainly involved in the cell cycle. Moreover, a positive correlation was noted between the high degree of infiltration with Th2 and CDC20.

Conclusion

High expression of CDC20 predicted poor survival, as potential target in the treatment for HCC.

The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis

INTRODUCTION

Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC.

METHODS

The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system.

RESULTS

363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan-Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38-4.80), 2 points: 3.91 (2.11-7.24), and 3 points: 5.09 (2.70-9.58), p < 0.01).

CONCLUSION

Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.

Whole‑genome identification and systematic analysis of lncRNA‑mRNA co‑expression profiles in patients with cutaneous basal cell carcinoma

Cutaneous basal cell carcinoma (BCC) is a common subtype of malignant skin tumor with low invasiveness. Early diagnosis and treatment of BCC and the identification of specific biomarkers are particularly urgent. Long non‑coding RNAs (lncRNAs) have been shown to be associated with the development of various tumors, including BCC. The present study conducted a comparative analysis of the differential expression of lncRNAs and mRNAs through whole‑genome technology. Microarray analyses were used to identify differentially expressed (DE) lncRNAs and DE mRNAs. Reverse transcription‑quantitative (RT‑q) PCR confirmed the differential expression of 10 lncRNAs in BCC. Subsequently, a lncRNA‑mRNA co‑expression network was constructed using the top 10 DE lncRNAs. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the possible biological effects of the identified mRNAs and to speculate on the possible biological effects of the lncRNAs. A total of 1,838 DE lncRNAs and 2,010 DE mRNAs were identified and 10 of the DE lncRNAs were confirmed by RT‑qPCR. A lncRNA‑mRNA co‑expression network comprising 166 specific co‑expressed lncRNAs and mRNAs was constructed using the top 10 DE lncRNAs. According to the results of the GO and KEGG analyses, lncRNA XR_428612.1 may serve an important role in mitochondrial dysfunction and the progression of BCC by modulating TICAM1, USMG5, COX7A2, FBXO10, ATP5E and TIMM8B. The present study provided whole‑genome identification and a systematic analysis of lncRNA‑mRNA co‑expression profiles in BCC.

Knockdown of long non-coding RNA TPT1-AS1 inhibits invasion and migration of hepatocarcinoma cells

BACKGROUND

The long non-coding RNA (lncRNA) TPT1-AS1 has been proved to affect the migration and invasion of tumor cells by different means, but its specific role and related mechanisms in hepatic carcinoma still need further research.

AIM

To investigate the expression of TPT1-AS1 in hepatocarcinoma tissues and cell lines and explore its biological role in the invasion and migration of hepatocarcinoma cells.

METHODS

Real-time quantitative PCR was used to measure lncRNA TPT1-AS1 expression in hepatocarcinoma tissue and cell lines (Huh7, SMMC-7721, HCCLM3, and HepG2). After being transfected with small interfering RNA (siRNA-TPT1-AS1), the invasion and migration of HepG2 cells were detected by transwell assay and wound healing assay. Western blot was used to measure the epithelial-mesenchymal transition (EMT) process and the activity of the PI3K/AKT pathway.

RESULTS

TPT1-AS1 was up-regulated in hepatopcarcinoma tissues and cell lines Huh7, SMMC-7721, HCCLM3, and HepG2. Transfection with siRNA-TPT1-AS1 noticeably restrained HepG2 cell invasion and migration, and suppressed the EMT process. Furthermore, TPT1-AS1 knockdown reduced MMP-9 expression and inhibited the activation of the PI3K/AKT pathway.

CONCLUSION

TPT1-AS1 is up-n regulated in hepatic carcinoma. Knockdown of TPT1-AS1 inhibits the invasion and migration of HepG2 cells via mechanisms that may be associated with reducing the activity of PI3K/AKT pathway and the expression of its downstream gene MMP-9, and inhibiting the EMT process.

Highly deregulated lncRNA LOC is associated with overall worse prognosis in Hepatocellular Carcinoma patients

Although numerous long non-coding RNAs (lncRNAs) were reported to be deregulated in Hepatocellular Carcinoma (HCC), experimentally characterized, and/or associated with patient's clinical characteristics, there is, thus far, minimal concerted research strategy to identify deregulated lncRNAs that modulate prognosis of HCC patients. Here, we present a novel strategy where we identify lncRNAs, which are not only de-regulated in HCC patients, but are also associated with pertinent clinical characteristics, potentially contributing to the prognosis of HCC patients. LOC101926913 (LOC) was further characterized because it is the most highly differentially expressed amongst those that are associated with the most number of clinical features (tumor-stage, vascular and tumor invasion and poorer overall survival). Experimental gain- and loss-of-function manipulation of LOC in liver cell-lines highlight LOC as a potential onco-lncRNA promoting cell proliferation, anchorage independent growth and invasion. LOC expression in cells up-regulated genes involved in GTPase-activities and downregulated genes associated with cellular detoxification, oxygen- and drug-transport. Hence, LOC may represent a novel therapeutic target, modulating prognosis of HCC patients through up-regulating GTPase-activities and down-regulating detoxification, oxygen- and drug-transport. This strategy may thus be useful for the identification of clinically relevant lncRNAs as potential biomarkers/targets that modulate prognosis in other cancers as well.

Role of lncRNA NR2F1-AS1 and lncRNA H19 Genes in Hepatocellular Carcinoma and Their Effects on Biological Function of Huh-7

Objective

This research was designed to probe into the expression and related mechanism of lncRNA NR2F1-AS1 and H19 in hepatocellular carcinoma (HCC).

Methods

Forty-two HCC patients who came to our hospital from February 2018 to August 2019 were included into a research group (RG). Meanwhile, 46 healthy controls were regarded as a control group (CG). BEL-7402, Huh-7 human hepatoma cells and HL-7702 human normal liver cells were purchased, and the NR2F1-AS1 and H19 levels in serum and tissues of HCC patients were detected. PcDNA3.1-NR2F1-AS1, si-NR2F1-AS1, NC, pcDNA3.1-H19 and si-H19 were transfected into BEL-7402 and Huh-7 cells. The NR2F1-AS1 and H19 levels in samples were detected via qRT-PCR, and the expression of apoptosis-related proteins in cells was tested through WB. Cell proliferation, invasion, or apoptosis was detected by CCK8, Transwell or flow cytometry, respectively.

Results

The NR2F1-AS1 and H19 levels were high in human hepatoma cells, and AUCs of lncRNA NR2F1-AS1 and lncRNA H19 were both >0.8. The lncRNA NR2F1-AS1 and lncRNA H19 were associated with HCC staging. After transfection of pcDNA3.1-NR2F1-AS1, si-NR2F1-AS1, NC, pcDNA3.1-H19, si-H19 BEL-7402 and Huh-7 cells, silencing NR2F1-AS1 and H19 expression can promote apoptosis and inhibit cell growth, while silencing their over-expression can inhibit the EMT process of Huh-7 cells.

Conclusion

lncRNA NR2F1-AS1 and lncRNA H19 genes are abnormally expressed in HCC. Furthermore, the two can suppress the EMT process of Huh-7 cells and promote apoptosis effectively.

Differentially expressed lncRNAs in liver tissues of TX mice with hepatolenticular degeneration

Wilson's Disease (WD), an ATP7B-mutated inherited disease that affects copper transport, is characterised by liver and nervous system manifestations. Long non-coding (ln-c) RNAs are widely involved in almost all physiological and pathological processes in the body, and are associated with numerous diseases. The present study aimed to elucidate the lncRNA-mRNA regulation network in a TX WD mouse model using RNA sequencing (RNA-seq). lncRNA expression profiles were screened using RNA-seq and real-time polymerase chain reaction, and differentially expressed lncRNAs and mRNAs were identified. To analyse the biological functions and pathways for the differentially expressed mRNAs, gene ontology and pathway enrichment analyses were performed. A significantly correlated lncRNA-mRNA relationship pair was calculated by CNC analysis to construct differential lncRNA and mRNA co-expression networks. A total of 2564 significantly up-regulated and 1052 down-regulated lncRNAs, and 1576 up-regulated and 297 down-regulated mRNAs, were identified. These genes were found to be associated with key processes such as apoptosis, and KEGG analysis revealed enrichment in the drug metabolism-cytochrome P450 pathway, PPAR signalling pathway, Notch signalling pathway, and MAPK signalling pathway. The identified differential lncRNAs may be involved in the pathogenesis and development of WD liver injury.