Screening and verification of long noncoding RNA promoter methylation sites in hepatocellular carcinoma

Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.

Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.

Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.

Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

Long Non-Coding RNA TRIM52-AS1 Promotes Growth and Metastasis via miR-218-5p/ROBO1 in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a malignant disease with a high mortality among primary HCC patients worldwide. Lots of studies have shown that lncRNAs are known as the biomarkers in diagnosis, treatment and prognosis of hepatocellular carcinoma. Therefore, clarifying the detailed function and mechanism of the lncRNA in the HCC progressing seems particularly important.

Methods

The TCGA and GEO database and RT-qPCR were used to analyse the expression of TRIM52-AS1 in HCC tissues and cell lines. Clinical data were collected to further analyze the correlation between indicators of clinical samples and the expression of TRIM52-AS1. CCK-8, plate clone and transwell assays were employed to evaluate the role of TRIM52-AS1 on cell proliferation, migration and invasion. Then, bioinformatics prediction, luciferase reporter, RNA immunoprecipitation (RIP), and RT-qPCR were employed to analyze the direct interaction among TRIM52-AS1, miR-218-5p and ROBO1. Additionally, the rescue function assays were used to verify that miR-218-5p/ROBO1 was the function downstream of TRIM52-AS1.

Results

TRIM52-AS1 was overexpressed in HCC according to the TCGA database and RT-qPCR assay. The expression of TRIM52-AS1 was higher in the metastatic foci compared with primary tumor according to the GEO database. Additionally, TRIM52-AS1 knockdown inhibited the proliferation and metastasis of HCC cells. TRIM52-AS1 could act as competitive endogenous RNA to regulate ROBO1 through miR-218-5p, then promoted the HCC cell progression.

Conclusion

TRIM52-AS1 is overexpressed in HCC and can promote the proliferation and metastasis of HCC cells through miR-218-5p/ROBO1 axis, then drives the HCC cell progression.

The Biological Roles of Exosomal Long Non-Coding RNAs in Cancers

Although it has many treatment strategies, cancer is still one of the most common causes of morbidity and mortality in the world. Exosomes are small extracellular vesicles (EVs) that can be secreted by almost all cells. Exosomes can encapsulate various types of molecules, including lipids, proteins, DNA, messenger RNAs, and non-coding RNAs [microRNAs (miRNAs) and long non-coding RNAs (lncRNAs)]. Exosome release is a way of communication between cells. They act as powerful signaling molecules between cancer cells and the surrounding cells that make up the cancer microenvironment. lncRNAs are a class of non-coding P, with a length of more than 200 bp, which are differentially expressed in many cancers. lncRNAs have been widely regarded as a new medium for cancer behavior. The presence of lncRNAs in circulation can be acellular or encapsulated in exosomal bodies released by cancer cells. Exosomal lncRNAs are functional and can transmit different phenotypic patterns to neighboring cells. Here, we reviewed the molecular mechanism of exosomal lncRNAs in regulating cancer progression, angiogenesis, and chemotherapy resistance, as well as the prospective applications of exosomal lncRNAs in cancer diagnosis, treatment and prognosis. These findings potentially promote the current understanding of exosomal lncRNAs and provide a new research direction for exosomal lncRNAs in cancer prevention, diagnosis, and treatment.

Immunohistochemical basigin expression level in thyroid cancer tissues

Background

Thyroid cancer (TC) is the most common endocrine malignancy; basigin (also known as BSG) plays a crucial role in tumor cell invasion, metastasis, and angiogenesis. This study was designed to identify the change of BSG expression in TC and its possible potential mechanism.

Methods

The BSG expression levels in TC were demonstrated using data collected from in-house immunohistochemical (IHC), RNA-sequencing (RNA-seq), microarrays, and literatures. Integrated analysis was performed to determined BSG expression levels in TC comprehensively. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the integration of BSG co-expressed genes and differentially expressed genes (DEGs) in TC tissues to explore the potential mechanisms of BSG in TC.

Results

The protein expression level of BSG was significantly higher in TC cases based on the IHC experiments. In addition, the combined SMD for BSG expression was 0.39 (p < 0.0001), the diagnostic odds ratio was 3.69, and the AUC of the sROC curve was 0.6986 using 1182 TC cases and 437 non-cancerous cases from 17 independent datasets. Furthermore, BSG co-expressed genes tended to be enriched in gene terms of the extracellular matrix (ECM), cell adhesion, and cell-cell interactions. The expression levels of nine hub BSG co-expressed genes were markedly upregulated in TC cases.

Conclusion

BSG expression levels were closely correlated with the progression of TC and may affect the signals of the ECM, cell adhesion, and cell-cell interactions.