Analysis of Hepatocellular Carcinoma Tissue for Biomarker Discovery

Upregulated TUBG1 expression is correlated with poor prognosis in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) development is a complex pathological process. Tubulin gamma 1 (TUBG1) plays an oncogenic role in several human cancers; however, its functional role in HCC tumorigenesis remains unknown.

Methods

Herein we first evaluated the gene expression levels of TUBG1 in HCC using data from The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases. We then elucidated the association between TUBG1 gene expression levels and survival rates of patients with HCC. Cell cycle, proliferation, transwell migration, and matrigel invasion assays were used to study the effects of TUBG1 on the malignant phenotypes of HCC cells.

Results

Based on the data obtained from the aforementioned databases and our in vitro experiments, TUBG1 was found to be overexpressed in HCC and patients with high TUBG1 expression levels showed a remarkably poor overall survival rate. In addition, the expression of TUBG1 significantly promoted the malignant phenotypes of HCC cells in vitro. Gene ontology term enrichment analysis revealed that co-regulated genes were enriched in biological processes mainly involved in chromosome segregation, chromosomal region, and chromatin binding; moreover, Kyoto Encyclopedia of Genes and Genome pathway analysis showed that they were mainly involved in cell cycle, oocyte meiosis, platinum drug resistance, and the p53 signaling pathway.

Conclusions

We report that TUBG1 is an important oncogene in HCC. It promotes HCC progression and may serve as a potential prognostic biomarker for HCC. Future studies are warranted to unveil molecular biological mechanisms underlying TUBG1 carcinogenesis.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

Upregulated LINC01667 Expression Is Correlated With Poor Prognosis in Hepatocellular Carcinoma

The development of hepatocellular carcinoma (HCC) is a complex pathological process. Long intergenic non–protein-coding RNA 1667 (LINC01667, also known as MGC38584) plays an oncogenic role in several human cancers; however, its functional role in HCC tumorigenesis remains unknown. Here, we first evaluated the gene expression levels of LINC01667 in HCC using data from The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then elucidated the association between LINC01667 gene expression levels and the survival rates of patients with HCC. We detected the effect of LINC01667 on the malignant phenotypes (cell proliferation, migration, invasion and apoptosis etc.) and the MAPK and PI3K/AKT/mTOR signaling pathways of HepG2, SMMC-7721 and HUH7 cells. We also analyzed the sensitivity of HepG2, SMMC-7721 and HUH7 with different expression levels of LINC01667 to anti-HCC drugs in vitro. Based on data from the aforementioned databases and our experiments in vitro, we found that LINC01667 was overexpressed in HCC, and that patients with high LINC01667 levels had a remarkably poor overall survival rate. In addition, inhibition of LINC01667 expression suppressed the proliferation, migration and invasion of HepG2 and SMMC-7721 cells and promoted their apoptosis in vitro. In contrast, overexpression of LINC01667 promoted the proliferation, migration and invasion of HUH7 cells and suppressed their apoptosis in vitro. ChIRP-seq (chromatin isolation by RNA purification) showed that LINC01667 bound to MEG3, and downregulated the expression of MEG3. In addition, western blotting showed that LINC01667 could activate the NF-κB pathway to promote cancer progression. In conclusion, we report that LINC01667 is an important oncogene in HCC and may be used as a potential diagnostic and prognostic biomarker of HCC.

HMGCS2 in metabolic pathways was associated with overall survival in hepatocellular carcinoma: A LASSO-derived study

This integrated bioinformatic study aimed to investigate potential prognostic candidates in hepatocellular carcinoma (HCC). In the GSE14520, GSE101685, and The Cancer Genome Atlas (TCGA) datasets, differentially expressed genes (DEGs) were identified and functional pathways of common DEGs were enriched. The least absolute shrinkage and selection operator (LASSO) model was used to screen the potential parameters associated with overall survival (OS) in HCC patients. Metabolic pathways were the most significantly enriched functional pathways of common DEGs in these three datasets. After LASSO model analysis, HMGCS2, UGP2, BCLC staging and TNM staging were screened as potential prognostic candidates for OS in HCC patients in GSE14520. HMGCS2 in the metabolic pathway was significantly downregulated in tumor tissues and peripheral blood mononuclear cells in HCC patients (all p < 0.05). Cox regression model indicated that HMGCS2 might be associate with OS in HCC patients in GSE14520 and in the TCGA (p = 0.029 and p = 0.05, respectively). Kaplan-Meier analysis demonstrated that HMGCS2 downregulation in tumors contributed to an unfavorable OS in HCC patients, both in GSE14520 and in the TCGA (p = 0.0001 and p = 0.0002, respectively). Additionally, HMGCS2 was significantly downregulated in HCC patients with high alpha-fetoprotein (AFP), main tumor size >5 cm, multinodular, advanced tumor staging including BCLC, TNM and CLIP (all p < 0.05). HMGCS2 was involved in metabolic pathways, and downregulated HMGCS2 in tumors was associated with unfavorable OS in HCC patients.

Robust Biomarker Screening Using Spares Learning Approach for Liver Cancer Prognosis

LncRNAs, miRNAs, mRNAs, methylation, and proteins exert profound biological functions and are widely applied as prognostic features in liver cancer. This study aims to identify prognostic biomarkers' signature for liver cancer. Samples with inadequate tumor purity were filtered out and the expression data from different resources were retrieved. The Spares learning approach was applied to select lncRNAs, miRNAs, mRNAs, methylation, and proteins' features based on their differentially expressed groups. The LASSO boosting technique was employed for the predictive model construction. A total of 200 lncRNAs, 200 miRNAs, 371 mRNAs, 371 methylations, and 184 proteins were observed to be differentially expressed. Five lncRNAs, 11 miRNAs, 30 mRNAs, 4 methylations, and 3 proteins were selected for further evaluation using the feature elimination technique. The highest accuracy of 89.32% is achieved as a result of training and learning by Spares learning methodology. Final outcomes revealed that 5 lncRNA, 11 miRNA, 30 mRNA, 4 methylation, and 3 protein signatures could be potential biomarkers for the prognosis of liver cancer patients.