A transcriptome sequencing study on the effect of macro-pores in hydrogel scaffolds on global gene expression of laden human cartilage chondrocytes

The macro-porous hydrogel scaffolds can not only enhance the proliferation of laden chondrocytes but also favor the deposition of hyaline cartilaginous extracellular matrix, however, the underlying molecular mechanism is still unclear. Herein, the global gene expression of human cartilage chondrocytes (HCCs) encapsulated in traditional hydrogel (Gel) constructs and micro-cavitary gel (MCG) constructs are investigated by using high-throughput RNA sequencing (RNA-seq). The differentially expressed genes (DEGs) between the HCCs cultured in Gel and MCG constructs have been identified via bioinformatics analysis. Significantly, the DEGs that promote cell proliferation (e.g. POSTN, MKI67, KIF20A) or neo-cartilage formation (e.g. COL2, ASPN, COMP, FMOD, FN1), are more highly expressed in MCG constructs than in Gel constructs, while the expressions of the DEGs associated with chondrocyte hypertrophy (e.g. EGR1, IBSP) are upregulated in Gel constructs. The expression of representative DEGs is verified at both mRNA and protein levels. Besides, cellular viability and morphology as well as the enriched signaling pathway of DEGs are studied in detail. These results of this work may provide data for functional tissue engineering of cartilage.

Screening and identification of key biomarkers in lung squamous cell carcinoma by bioinformatics analysis

The high mortality rate of lung squamous cell carcinoma (LUSC) is in part due to the lack of early detection of its biomarkers. The identification of key molecules involved in LUSC is therefore required to improve clinical diagnosis and treatment outcomes. The present study used the microarray datasets GSE31552, GSE6044 and GSE12428 from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to construct the protein-protein interaction network of DEGs and hub genes module using STRING and Cytoscape. The 67 DEGs identified consisted of 42 upregulated genes and 25 downregulated genes. The pathways predicted by KEGG and GO enrichment analyses of DEGs mainly included cell cycle, cell proliferation, glycolysis or gluconeogenesis, and tetrahydrofolate metabolic process. Further analysis of the University of California Santa Cruz and ONCOMINE databases identified 17 hub genes. Overall, the present study demonstrated hub genes that were closely associated with clinical tissue samples of LUSC, and identified TYMS, CCNB2 and RFC4 as potential novel biomarkers of LUSC. The findings of the present study contribute to an improved understanding of the molecular mechanisms of carcinogenesis and progression of LUSC, and assist with the identification of potential diagnostic and therapeutic targets of LUSC.

Using Naïve Bayes Algorithm to Estimate the Response to Drug in Lung Cancer Patients

AIM AND OBJECTIVE

Lung cancer is a highly heterogeneous cancer, due to the significant differences in molecular levels, resulting in different clinical manifestations of lung cancer patients there is a big difference. Including disease characterization, drug response, the risk of recurrence, survival, etc.

Method

Clinical patients with lung cancer do not have yet particularly effective treatment options, while patients with lung cancer resistance not only delayed the treatment cycle but also caused strong side effects. Therefore, if we can sum up the abnormalities of functional level from the molecular level, we can scientifically and effectively evaluate the patients' sensitivity to treatment and make the personalized treatment strategies to avoid the side effects caused by over-treatment and improve the prognosis.

RESULT & CONCLUSION

According to the different sensitivities of lung cancer patients to drug response, this study screened out genes that were significantly associated with drug resistance. The bayes model was used to assess patient resistance.

MiR-3613-3p affects cell proliferation and cell cycle in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common types of malignant tumors with poor sensitivity to chemotherapy drugs and poor prognosis among patients. In the present study, we downloaded the original data from the Gene Expression Omnibus and compared gene expression profiles of liver cancer cells in patients with HCC with those of colon epithelial cells of healthy controls to identify differentially expressed genes (DEGs). After filtering target microRNAs (miRNA) from core DEGs, we cultured HepG2 cells in vitro, knocked down the miRNA and core mRNAs, and analyzed the effects. We found 228 differentially expressed genes between liver cancer tissue and healthy control tissue. We also integrated the protein-proteininteraction network and module analysis to screen 13 core genes, consisting of 12 up-regulated genes and 1 down-regulated gene. Five core genes were regulated hsa-miR-3613-3p, therefor we hypothesized that hsa-miR-3613-3p was a critical miRNA. After the transfection procedure, we found that changes in hsa-miR-3613-3p were the most obvious. Therefore, we speculated that hsa-miR-3613-3p was a main target miRNA. In addition, we transfected with si (BIRC5, CDK1, NUF2, ZWINT and SPC24), to target genes that can be targeted by miR-3613-3p. Our data shows that BIRC5, NUF2, and SPC24 may be promising liver cancer biomarkers that may not only predict disease occurrence but also potential personalized treatment options.