Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer

BACKGROUND

The occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level.

METHODS

In this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis.

RESULTS

The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes.

CONCLUSIONS

These genes could provide a valuable reference in clinical treatment among breast cancer patients.

Single‐cell RNA sequencing reveals the multi‐cellular ecosystem in different radiological components of pulmonary part‐solid nodules

Background

Early‐stage lung adenocarcinoma that radiologically manifests as part‐solid nodules, consisting of both ground‐glass and solid components, has distinctive growth patterns and prognosis. The characteristics of the tumour microenvironment and transcriptional features of the malignant cells of different radiological phenotypes remain poorly understood.

Methods

Twelve treatment‐naive patients with radiological part‐solid nodules were enrolled. After frozen pathology was confirmed as lung adenocarcinoma, two regions (ground‐glass and solid) from each of the 12 part‐solid nodules and 5 normal lung tissues from 5 of the12 patients were subjected to single‐cell sequencing by 10x Genomics. We used Seurat v3.1.5 for data integration and analysis.

Results

We comprehensively dissected the multicellular ecosystem of the ground‐glass and solid components of part‐solid nodules at the single‐cell resolution. In tumours, these components had comparable proportions of malignant cells. However, the angiogenesis, epithelial‐to‐mesenchymal transition, KRAS, p53, and cell‐cycle signalling pathways were significantly up‐regulated in malignant cells within solid components compared to those within ground‐glass components. For the tumour microenvironment, the relative abundance of myeloid and NK cells tended to be higher in solid components than in ground‐glass components. Slight subtype composition differences existed between the ground‐glass and solid components. The T/NK cell subsets’ cytotoxic function and the macrophages’ pro‐inflammation function were suppressed in solid components. Moreover, pericytes in solid components had a stronger communication related to angiogenesis promotion with endothelial cells and tumour cells.

Conclusion

The cellular landscape of ground‐glass components is significantly different from that of normal tissue and similar to that of solid components. However, transcriptional differences exist in the vital signalling pathways of malignant and immune cells within these components.

Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

The Acidic Tumor Microenvironment Affects Epithelial-Mesenchymal Transition Markers as Well as Adhesion of NCI-H358 Lung Cancer Cells

Epithelial-mesenchymal transition (EMT), which is involved in metastasis formation, requires reprogramming of gene expression mediated by key EMT transcription factors. However, signals from the cellular microenvironment, including hypoxia, can also modulate the process of EMT. Hypoxia is often associated with a reduction in the extracellular pH of the tumor microenvironment (acidosis). Whether acidosis alone has an impact on the expression of the EMT markers E-cadherin, N-cadherin, and vimentin was studied in NCI-H358 lung cancer cells. Reducing extracellular pH decreased E-cadherin mRNA, while vimentin and N-cadherin mRNA were doubled. However, at the protein level, E-cadherin and N-cadherin were both reduced, and only vimentin was upregulated. E-cadherin and N-cadherin expression at the cell surface, which is the relevant parameter for cell-cell and cell-matrix interaction, decreased too. The reduction of cell surface proteins was due to diminished protein expression and not changes in cellular localization, since localization of EMT markers in general was not affected by acidosis. Acidosis also affected NCI-H358 cells functionally. Adhesion was decreased when the cells were primed in an acidic medium before measuring cell adherence, which is in line with the reduced expression of cadherins at the cell surface. Additionally, migration was decreased after acidic priming. A possible mechanism for the regulation of EMT markers involves the action of microRNA-203a (miR-203a). In NCI-H358 lung cancer cells, miR-203a expression was repressed by acidosis. Since a decrease in the level of miR-203a has been shown to induce EMT, it might be involved in the modulation of EMT marker expression, adhesion, and migration by the acidic tumor microenvironment in NCI-H358 lung cancer cells.

Molecular Signatures and Networks of Cardiomyocyte Differentiation in Humans and Mice

Cardiomyocyte differentiation derived from embryonic stem cells (ESCs) is a complex process involving molecular regulation of multiple levels. In this study, we first identify and compare differentially expressed gene (DEG) signatures of ESC-derived cardiomyocyte differentiation (ESCDCD) in humans and mice. Then, the multiscale embedded gene co-expression network analysis (MEGENA) of the human ESCDCD dataset is performed to identify 212 significantly co-expressed gene modules, which capture well the regulatory information of cardiomyocyte differentiation. Three modules respectively involved in the regulation of stem cell pluripotency, Wnt, and calcium pathways are enriched in the DEG signatures of the differentiation phase transition in the two species. Three human-specific cardiomyocyte differentiation phase transition modules are identified. Moreover, the potential regulation mechanisms of transcription factors during cardiomyocyte differentiation are also illustrated. Finally, several novel key drivers of ESCDCD are identified with the evidence of their expression during mouse embryonic cardiomyocyte differentiation. Using an integrative network analysis, the core molecular signatures and gene subnetworks (modules) underlying cardiomyocyte lineage commitment are identified in both humans and mice. Our findings provide a global picture of gene-gene co-regulation and identify key regulators during ESCDCD.

Support Vector Machine for Lung Adenocarcinoma Staging Through Variant Pathways

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors. How to effectively diagnose LUAD at an early stage and make an accurate judgement of the occurrence and progression of LUAD are still the focus of current research. Support vector machine (SVM) is one of the most effective methods for diagnosing LUAD of different stages. The study aimed to explore the dynamic change of differentially expressed genes (DEGs) in different stages of LUAD, and to assess the risk of LUAD through DEGs enriched pathways and establish a diagnostic model based on SVM method. Based on TMN stages and gene expression profiles of 517 samples in TCGA-LUAD database, coefficient of variation (CV) combined with one-way analysis of variance (ANOVA) were used to screen out feature genes in different TMN stages after data standardization. Unsupervised clustering analysis was conducted on samples and feature genes. The feature genes were analyzed by Pearson correlation coefficient to construct a co-expression network. Fisher exact test was conducted to verify the most enriched pathways, and the variation of each pathway in different stages was analyzed. SVM networks were trained and ROC curves were drawn based on the predicted results so as to evaluate the predictive effectiveness of the SVM model. Unsupervised hierarchical clustering analysis results showed that almost all the samples in stage III/IV were clustered together, while samples in stage I/II were clustered together. The correlation of feature genes in different stages was different. In addition, with the increase of malignant degree of lung cancer, the average shortest path of the network gradually increased, while the closeness centrality gradually decreased. Finally, four feature pathways that could distinguish different stages of LUAD were obtained and the ability was tested by the SVM model with an accuracy of 91%. Functional level differences were quantified based on the expression of feature genes in lung cancer patients of different stages, so as to help the diagnosis and prediction of lung cancer. The accuracy of our model in differentiating between stage I/II and stage III/IV could reach 91%.

A Novel Approach for Quantifying Cancer Cells Showing Hybrid Epithelial/Mesenchymal States in Large Series of Tissue Samples: Towards a New Prognostic Marker

In cancer biology, epithelial-to-mesenchymal transition (EMT) is associated with tumorigenesis, stemness, invasion, metastasis, and resistance to therapy. Evidence of co-expression of epithelial and mesenchymal markers suggests that EMT should be a stepwise process with distinct intermediate states rather than a binary switch. In the present study, we propose a morphological approach that enables the detection and quantification of cancer cells with hybrid E/M states, i.e., which combine partially epithelial (E) and partially mesenchymal (M) states. This approach is based on a sequential immunohistochemistry technique performed on the same tissue section, the digitization of whole slides, and image processing. The aim is to extract quantitative indicators able to quantify the presence of hybrid E/M states in large series of human cancer samples and to analyze their relationship with cancer aggressiveness. As a proof of concept, we applied our methodology to a series of about a hundred urothelial carcinomas and demonstrated that the presence of cancer cells with hybrid E/M phenotypes at the time of diagnosis is strongly associated with a poor prognostic value, independently of standard clinicopathological features. Although validation on a larger case series and other cancer types is required, our data support the hybrid E/M score as a promising prognostic biomarker for carcinoma patients.

Extracellular Acidosis Modulates the Expression of Epithelial-Mesenchymal Transition (EMT) Markers and Adhesion of Epithelial and Tumor Cells

Epithelial-to-mesenchymal transition (EMT) is an important process of tumor progression associated with increased metastatic potential. EMT can be activated by external triggers such as cytokines or metabolic parameters (e.g. hypoxia). Since extracellular acidosis is a common finding in tumors, the aim of the study is to analyze its impact on the expression of EMT markers in vitro and in vivo as well as the functional impact on cell adhesion. Therefore, three tumor and two normal epithelial cell lines were incubated for 24 h at pH 6.6 and the expression of EMT markers was studied. In addition, mRNA expression of transcription and metabolic factors related to EMT was measured as well as the functional impact on cell adhesion, either during acidic incubation or after priming cells in an acidic milieu. E-cadherin and N-cadherin were down-regulated in all tumor and normal cell lines studied, whereas vimentin expression increased in only two tumor and one normal cell line. Down-regulation of the cadherins was seen in total protein and to a lesser extent in surface protein. In vivo an increase in N-cadherin and vimentin expression was found. Acidosis up-regulated Twist1 and Acsl1 but down-regulated fumarate hydratase (Fh). Cell adhesion during acidic incubation decreased in AT1 prostate carcinoma cells whereas preceding acidic priming increased their subsequent adhesion. Low tumor pH is able to modulate the expression EMT-related proteins and by this may affect the stability of the tissue structure.