Prognostic significance of TCF21 mRNA expression in patients with lung adenocarcinoma

Circulating Tumour Cells (CTCs) in NSCLC: From Prognosis to Therapy Design

Designing optimal (neo)adjuvant therapy is a crucial aspect of the treatment of non-small-cell lung carcinoma (NSCLC). Standard methods of chemotherapy, radiotherapy, and immunotherapy represent effective strategies for treatment. However, in some cases with high metastatic activity and high levels of circulating tumour cells (CTCs), the efficacy of standard treatment methods is insufficient and results in treatment failure and reduced patient survival. CTCs are seen not only as an isolated phenomenon but also a key inherent part of the formation of metastasis and a key factor in cancer death. This review discusses the impact of NSCLC therapy strategies based on a meta-analysis of clinical studies. In addition, possible therapeutic strategies for repression when standard methods fail, such as the administration of low-toxicity natural anticancer agents targeting these phenomena (curcumin and flavonoids), are also discussed. These strategies are presented in the context of key mechanisms of tumour biology with a strong influence on CTC spread and metastasis (mechanisms related to tumour-associated and -infiltrating cells, epithelial–mesenchymal transition, and migration of cancer cells).

Computational pan-cancer characterization of model-based quantitative transcription regulations dysregulated in regional lymph node metastasis

Cancer is one of the major causes of mortality worldwide. Regional lymph node metastasis is an important mechanism during the spread of human cancers, in which transcription regulation plays an essential role. This study formulated a regression-model-based quantitative transcription regulation (mqTrans) between one mRNA gene and multiple transcription factors (TFs). Computational pan-cancer screening was carried out to detect the quantitative dysregulation of transcription regulation in the regional lymph node metastasis of 18 cancer types. Only a few metastasis-dysregulated mqTrans models were shared among the cancer types. The mRNA genes of the metastasis-dysregulated mqTrans models were not differentially expressed in regional lymph node metastasis. The experimental data suggested that mqTrans technology provided a complementary approach to the evaluation of transcription regulation mechanisms and may facilitate its quantitative investigation in other phenotypes.

S100 calcium binding protein A6 and associated long noncoding ribonucleic acids as biomarkers in the diagnosis and staging of primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is a chronic and slowly progressing cholestatic disease, which causes damage to the small intrahepatic bile duct by immuno-regulation, and may lead to cholestasis, liver fibrosis, cirrhosis and, eventually, liver failure.

AIM

To explore the potential diagnosis and staging value of plasma S100 calcium binding protein A6 (S100A6) messenger ribonucleic acid (mRNA), LINC00312, LINC00472, and LINC01257 in primary biliary cholangitis.

METHODS

A total of 145 PBC patients and 110 healthy controls (HCs) were enrolled. Among them, 80 PBC patients and 60 HCs were used as the training set, and 65 PBC patients and 50 HCs were used as the validation set. The relative expression levels of plasma S100A6 mRNA, long noncoding ribonucleic acids LINC00312, LINC00472 and LINC01257 were analyzed using quantitative reverse transcription-polymerase chain reaction. The bile duct ligation (BDL) mouse model was used to simulate PBC. Then double immunofluorescence was conducted to verify the overexpression of S100A6 protein in intrahepatic bile duct cells of BDL mice. Human intrahepatic biliary epithelial cells were treated with glycochenodeoxycholate to simulate the cholestatic environment of intrahepatic biliary epithelial cells in PBC.

RESULTS

The expression of S100A6 protein in intrahepatic bile duct cells was up-regulated in the BDL mouse model compared with sham mice. The relative expression levels of plasma S100A6 mRNA, log10 LINC00472 and LINC01257 were up-regulated while LINC00312 was down-regulated in plasma of PBC patients compared with HCs (3.01 ± 1.04 vs 2.09 ± 0.87, P < 0.0001; 2.46 ± 1.03 vs 1.77 ± 0.84, P < 0.0001; 3.49 ± 1.64 vs 2.37 ± 0.96, P < 0.0001; 1.70 ± 0.33 vs 2.07 ± 0.53, P < 0.0001, respectively). The relative expression levels of S100A6 mRNA, LINC00472 and LINC01257 were up-regulated and LINC00312 was down-regulated in human intrahepatic biliary epithelial cells treated with glycochenodeoxycholate compared with control (2.97 ± 0.43 vs 1.09 ± 0.08, P = 0.0018; 2.70 ± 0.26 vs 1.10 ± 0.10, P = 0.0006; 2.23 ± 0.21 vs 1.10 ± 0.10, P = 0.0011; 1.20 ± 0.04 vs 3.03 ± 0.15, P < 0.0001, respectively). The mean expression of S100A6 in the advanced stage (III and IV) of PBC was up-regulated compared to that in HCs and the early stage (II) (3.38 ± 0.71 vs 2.09 ± 0.87, P < 0.0001; 3.38 ± 0.71 vs 2.57 ± 1.21, P = 0.0003, respectively); and in the early stage (II), it was higher than that in HCs (2.57 ± 1.21 vs 2.09 ± 0.87, P = 0.03). The mean expression of LINC00312 in the advanced stage was lower than that in the early stage and HCs (1.39 ± 0.29 vs 1.56 ± 0.33, P = 0.01; 1.39 ± 0.29 vs 2.07 ± 0.53, P < 0.0001, respectively); in addition, the mean expression of LINC00312 in the early stage was lower than that in HCs (1.56 ± 0.33 vs 2.07 ± 0.53, P < 0.0001). The mean expression of log10 LINC00472 in the advanced stage was higher than those in the early stage and HCs (2.99 ± 0.87 vs 1.81 ± 0.83, P < 0.0001; 2.99 ± 0.87 vs 1.77 ± 0.84, P < 0.0001, respectively). The mean expression of LINC01257 in both the early stage and advanced stage were up-regulated compared with HCs (3.88 ± 1.55 vs 2.37 ± 0.96, P < 0.0001; 3.57 ± 1.79 vs 2.37 ± 0.96, P < 0.0001, respectively). The areas under the curves (AUC) for S100A6, LINC00312, log10 LINC00472 and LINC01257 in PBC diagnosis were 0.759, 0.7292, 0.6942 and 0.7158, respectively. Furthermore, the AUC for these four genes in PBC staging were 0.666, 0.661, 0.839 and 0.5549, respectively. The expression levels of S100A6 mRNA, log10 LINC00472, and LINC01257 in plasma of PBC patients were decreased (2.35 ± 1.02 vs 3.06 ± 1.04, P = 0.0018; 1.99 ± 0.83 vs 2.33 ± 0.96, P = 0.036; 2.84 ± 0.92 vs 3.69 ± 1.54, P = 0.0006), and the expression level of LINC00312 was increased (1.95 ± 0.35 vs 1.73 ± 0.32, P = 0.0007) after treatment compared with before treatment using the paired t-test. Relative expression of S100A6 mRNA was positively correlated with log10 LINC00472 (r = 0.683, P < 0.0001); serum level of collagen type IV was positively correlated with the relative expression of log10 LINC00472 (r = 0.482, P < 0.0001); relative expression of S100A6 mRNA was positively correlated with the serum level of collagen type IV (r = 0.732, P < 0.0001). The AUC for the four biomarkers obtained in the validation set were close to the training set.

CONCLUSION

These four genes may potentially act as novel biomarkers for the diagnosis of PBC. Moreover, LINC00472 acts as a potential biomarker for staging in PBC.

Clinicopathological and prognostic significance of circRNAs in lung cancer: A systematic review and meta-analysis

BACKGROUND

Circular RNAs (circRNAs) regulate multiple pathways during lung cancer pathogenesis. Apart from functional significance, many circRNAs have been shown to be associated with clinicopathological characteristics and predict lung cancer prognosis. Our aim is to summarize the expanding knowledge of clinical roles of circRNAs in lung cancer.

METHODS

A thorough search of literature was conducted to identify articles about the correlation between circRNA expression and its prognostic and clinicopathological values. Biological mechanisms were summarized.

RESULTS

This study included 35 original articles and 32 circRNAs with prognostic roles for lung cancer. Increased expression of 25 circRNAs and decreased expression of 7 circRNAs predicted poor prognosis. For non-small cell lung cancer, changes of circRNAs were correlated with tumor size, lymph node metastasis, distant metastasis, tumor node metastasis (TNM) stage, and differentiation, indicating the major function of circRNAs is to promote lung cancer invasion and migration. Particularly, meta-analysis of ciRS-7, hsa_circ_0020123, hsa_circ_0067934 showed increase of the 3 circRNAs was associated with positive lymph node metastasis. Increase of ciRS-7 and hsa_circ_0067934 was also related with advanced TNM stage. The biological effects depend on the general function of circRNA as microRNA sponge.

CONCLUSIONS

CircRNAs have the potential to function as prognostic markers and are associated with lung cancer progression and metastasis.

Interactive Verification Analysis of Multiple Sequencing Data for Identifying Potential Biomarker of Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) comprises around 40% of all lung cancers, and in about 70% of patients, it has spread locally or systemically when first detected leading to a worse prognosis.

Methods

We filtered out differentially expressed genes (DEGs) based on the RNA sequencing data in the Gene Expression Omnibus database and verified and deeply analyzed screened DEGs using a combined bioinformatics approach.

Results

Expressions of 11,143 genes in 694 nontumor lung tissues and LUAD cases from 8 independent laboratories were analyzed; 188 mRNAs were identified as differentially expressed genes (DEGs). A PPI network constructed with 188 DEGs screened out 8 hub DEGs (CDH5, PECAM1, VWF, CLDN5, COL1A1, MMP9, SPP1, and IL6) which highly interconnected with other nodes. The expression levels of 8 hub genes in LUAD and control were assessed in the Oncomine database, and the results were consistent. The survival curves of 8 hub genes showed that their expressions are significantly related to the prognosis of lung cancer and LUAD patients except for IL6. Since the expression of IL6 is nonspecific and highly sensitive, we choose the other 7 hub genes we had verified to do the next analysis. Mutual exclusivity or cooccurrence analysis of 7 hub genes identified a tendency towards cooccurrence between CDH5, PECAM1, and VWF in LUAD. The coexpression profiles of CDH5 in LUAD were identified, and we found that PECAM1 and VWF coexpressed with CDH5. Immunohistochemistry and RT-PCR analysis showed that higher levels of CDH5, PECAM1, and VWF were expressed in normal lung tissues but a low or undetectable level was found in LUAD tissues.

Conclusions

Taken together, we speculate that CDH5, PECAM1, and VWF played an important role in LUAD.

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

The magnitude of the immune response is closely associated with clinical outcome in patients with cancer. However, finding potential therapeutic targets for lung cancer in the immune system remains challenging. Here, we constructed a vital immune‐prognosis genes (VIPGs) based cluster of lung adenocarcinoma (LUAD) from IMMPORT databases and The Cancer Genome Atlas. A transcription factor regulatory network for the VIPGs was also established. The tumor microenvironment of LUAD was analyzed using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and single‐sample Gene Set Enrichment Analysis. The immune checkpoints and genomic alterations were explored in the different immune clusters. We identified 15 VIPGs for patients with LUAD and clustered the patients into low‐immunity and high‐immunity subtypes. The immune score, stromal score and ESTIMATE score were significantly higher in the high‐immunity subtype, whereas tumor purity was higher in the low‐immunity subtype. In addition, the immune checkpoints cytotoxic T lymphocyte associate protein‐4(CTLA4), programmed cell death protein‐1 and programmed death‐ligand were elevated in the low‐immunity subtype. The genomic results also showed that the tumor mutation burden was higher in the high‐immunity subtype. Finally, Gene Set Enrichment Analysis showed that several immune‐related gene sets, including interleukin‐2/STAT5 signaling, inflammatory response, interleukin‐6/Janus kinase(JAK)/signal transducer and activator of transcription 3 (STAT3) signaling, interferon‐gamma response and allograft rejection, were elevated in the high‐immunity subtype. Finally, high‐immunity patients exhibited greater overall and disease‐specific survival outcome compared with low‐immunity patients (log rank P = 0.013 and P = 0.0097). Altogether, here we have identified 15 immune‐prognosis genes and a potential immune subtype for patients with LUAD, which may provide new insights into the prognosis and treatment of LUAD.

Analysis of genes associated with prognosis of lung adenocarcinoma based on GEO and TCGA databases

BACKGROUNDS

Lung adenocarcinoma (LUAD) is one of the most common malignancies, and is a serious threat to human health. The aim of the present study was to assess potential biomarkers for the prognosis of LUAD through the analysis of gene expression microarrays.

METHODS

The gene expression data for GSE118370 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal lung and LUAD samples were screened using the R language. The DAVID database was used to analyze the functions and pathways of DEGs. The STRING database was used to the map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Finally, the prognostic analysis of the hub gene in the PPI network was performed using the Kaplan-Meier tool.

RESULTS

A total of 406 downregulated and 203 upregulated DEGs were identified. The GO analysis results revealed that downregulated DEGs were significantly enriched in angiogenesis, calcium ion binding and cell adhesion. The upregulated DEGs were significantly enriched in the extracellular matrix disassembly, collagen catabolic process, chemokine-mediated signaling pathway and endopeptidase inhibitor activity. The KEGG pathway analysis revealed that downregulated DEGs were enriched in neuroactive ligand-receptor interaction, hematopoietic cell lineage and vascular smooth muscle contraction, while upregulated DEGs were enriched in phototransduction. In addition, the top 10 hub genes and the most closely interacting modules of the top 3 proteins in the PPI network were screened. Finally, the independent prognostic value of each hub gene in LUAD patients was analyzed through the Kaplan-Meier plotter. Seven hub genes (ADCY4, S1PR1, FPR2, PPBP, NMU, PF4, and GCG) were closely correlated to overall survival time.

CONCLUSION

The discovery of these candidate genes and pathways reveals the etiology and molecular mechanisms of LUAD, providing ideas and guidance for the development of new therapeutic approaches to LUAD.

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Background

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists’ capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets.

Results

In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets’ ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets.

Conclusions

Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0642-2) contains supplementary material, which is available to authorized users.

Multiple biological functions of transcription factor 21 in the development of various cancers

Transcription factor 21 (TCF21) is a basic helix–loop–helix transcription factor that binds to DNA and regulates cell differentiation and cell fate specification through mesenchymal–epithelial transition during development. The TCF21 gene is epigenetically inactivated in many types of human cancers and exerts a wide variety of functions, including the regulation of epithelial–mesenchymal transition, invasion, metastasis, cell cycle, and autophagy. This review focuses on research progress in relation to the roles of TCF21 in tumor development. We systematically consider multiple pathological functions of TCF21 in various cancers, revealing the molecular bases of its diverse biological roles and providing new directions for future research.

Bioinformatics and functional analyses of key genes and pathways in human clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. The present study was conducted to explore the mechanisms and identify the potential target genes for ccRCC using bioinformatics analysis. The microarray data of GSE15641 were screened on Gene-Cloud of Biotechnology Information (GCBI). A total of 32 ccRCC samples and 23 normal kidney samples were used to identify differentially expressed genes (DEGs) between them. Subsequently, the clustering analysis and functional enrichment analysis of these DEGs were performed, followed by protein-protein interaction (PPI) network, and pathway relation network. Additionally, the most significant module based on PPI network was selected, and the genes in the module were identified as hub genes. Furthermore, transcriptional level, translational level and survival analyses of hub genes were performed to verify the results. A total of 805 genes, 403 upregulated and 402 downregulated, were differentially expressed in ccRCC samples compared with normal controls. The subsequent bioinformatics analysis indicated that the small molecule metabolic process and the metabolic pathway were significantly enriched. A total of 7 genes, including membrane metallo-endopeptidase (MME), albumin (ALB), cadherin 1 (CDH1), prominin 1 (ROM1), chemokine (C-X-C motif) ligand 12 (CXCL12), protein tyrosine phosphatase receptor type C (PTPRC) and intercellular adhesion molecule 1 (ICAM1) were identified as hub genes. In brief, the present study indicated that these candidate genes and pathways may aid in deciphering the molecular mechanisms underlying the development of ccRCC, and may be used as therapeutic targets and diagnostic biomarkers of ccRCC.