CDH2/N-cadherin and early diagnosis of invasion in patients with ductal carcinoma in situ

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Galbanic acid suppresses melanoma cell migration and invasion by reducing MMP activity and downregulating N-cadherin and fibronectin

High mortality rate of melanoma is due to the metastasis of malignant cells. Galbanic acid (GBA) is a natural sesquiterpene coumarin with valuable pharmaceutical activities. Our study aimed to investigate whether GBA can affect the migration, invasion, and adhesion of melanoma cells. The survival rate of B16F10 cells was measured using the alamarBlue assay. Scratch, adhesion, and invasion assays were performed to determine the effect of GBA on metastatic behavior of cells. Moreover, gelatin zymography was done to assess the activity of MMP-2 and MMP-9, and qRT-PCR was used to investigate the effect of GBA on the expression of candidate genes. Based on the results of alamarBlue assay, 40 µM GBA was chosen as the optimum concentration for all tests. Our findings indicated that GBA significantly decreased the invasion and migration of B16F10 cells while enhancing their adhesion ability. In addition, gelatin zymography demonstrated that GBA reduced the enzymatic activity of MMP-2 and MMP-9. Moreover, qRT-PCR revealed that GBA reduced the expression of N-cadherin and fibronectin. Current findings demonstrated, for the first time, that GBA inhibited the migration and invasion of melanoma cells via reducing the activity of MMP-2 and MMP-9 and downregulating N-cadherin and fibronectin expression. Accordingly, GBA could be suggested as a potential therapeutic agent for the treatment of melanoma.

Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets

Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma

PURPOSE

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC.

METHODS

TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways.

RESULTS

This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response.

CONCLUSION

Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Automated Classification of Breast Cancer Across the Spectrum of ERBB2 Expression Focusing on Heterogeneous Tumors With Low Human Epidermal Growth Factor Receptor 2 Expression

PURPOSE

Although pharmaceutical companies conduct clinical trials of novel human epidermal growth factor receptor 2 (HER2)-low-directed drugs, diagnosing HER2-low cancer by immunohistochemistry (IHC) and in situ hybridization (ISH) remains challenging. This study investigates the performance of first-in-kind computerized intelligence to classify samples across gene expression levels and differentiate HER2-low tumors.

MATERIALS AND METHODS

We classified 251 samples: 142 primary invasive breast cancers (IBCs), 75 ductal carcinomas in situ (DCIS), and 34 mammaplasties (reference) using mRNA expression data from the QuantiGene Plex 2.0 assay. We used g3mclass probabilistic software to assess the number of classes in the assay data, the mean and the variance in each class, diagnostic cutoffs, and the prevalence of each class in the study population.

RESULTS

HER2-low (IHC score of 1+ or 2+/ISH-) accounted for 31% of IBC. First, we discovered that HER2-low tumors were represented by cases with normal ERBB2 transcript levels that were expected to produce physiologic levels of HER2 (70%) and cases with abnormally upregulated unamplified ERBB2 (30%). We termed the latter cancers ERBB2-up as they do not meet the standard definitions for ERBB2 overexpression and amplification. Second, HER2-low IBC classified as ERBB2-up had not only abnormally increased luminal growth and adhesion markers (ERBB2, ESR1, PGR, IGF1R, VAV2, VAV3, KRT8, CDH1) but also downregulated myoepithelial marker (KRT5). The vascularization (RAP1 and C3G), immune cell infiltration (VAV1), and mesenchymal transition (CDH2) markers were dysregulated. Finally, in the independent cohort of DCIS, 40% of HER2-low DCIS shared similar traits with HER2-low IBC except for rare downregulation of KRT5 and no change in C3G, VAV1, and CDH2.

CONCLUSION

We demonstrated how innovative bioinformatic tools could help diagnose cancer across the spectrum of ERBB2 expression to aid decision making for HER2-low.

Epigenetic regulation of epithelial-mesenchymal transition during cancer development

Epithelial-mesenchymal transition (EMT) plays essential roles in promoting malignant transformation of epithelial cells, leading to cancer progression and metastasis. During EMT-induced cancer development, a wide variety of genes are dramatically modified, especially down-regulation of epithelial-related genes and up-regulation of mesenchymal-related genes. Expression of other EMT-related genes is also modified during the carcinogenic process. Especially, epigenetic modifications are observed in the EMT-related genes, indicating their involvement in cancer development. Mechanically, epigenetic modifications of histone, DNA, mRNA and non-coding RNA stably change the EMT-related gene expression at transcription and translation levels. Herein, we summarize current knowledge on epigenetic regulatory mechanisms observed in EMT process relate to cancer development in humans. The better understanding of epigenetic regulation of EMT during cancer development may lead to improvement of drug design and preventive strategies in cancer therapy.

The g3mclass is a practical software for multiclass classification on biomarkers

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output's accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.

Exploration of the breast ductal carcinoma in situ signature and its prognostic implications

Following the implementation of breast screening programs, the occurrence of ductal carcinoma in situ (DCIS) as an early type of neoplasia has increased. Although the prognosis is promising, 20%-50% of DCIS patients will progress to invasive ductal carcinoma (IDC) if not treated. It is essential to look for promising biomarkers for predicting DCIS prognosis. The Gene Expression Omnibus (GEO) database was used to explore the expression of genes that differed between DCIS and normal tissue in this investigation. Enrichment analysis was performed to characterize the biological role and intrinsic process pathway. The Cancer Genome Atlas Breast Cancer Dataset was used to categorize the hub genes, and the results were confirmed using the Cytoscape plugin CytoHubba and MCODE. The prognostic ability of the core gene signature was determined through time-dependent receiver operating characteristic (ROC), Kaplan-Meier survival curve, Oncomine databases, and UALCAN databases. In addition, the prognostic value of core genes was verified in proliferation assays. We identified 217 common differentially expressed genes (DEGs) in the present study, with 101 upregulated and 138 downregulated genes. The top genes were obtained from the PPI network (protein-protein interaction). A unique six-gene signature (containing GAPDH, CDH2, BIRC5, NEK2, IDH2, and MELK) was developed for DCIS prognostic prediction. Centered on the Cancer Genome Atlas (TCGA) cohort, the ROC curve showed strong results in prognosis prediction. The six core gene signatures is often overexpressed in DCIS, with a weak prognosis. Furthermore, when breast cancer cells are transfected with small interfering RNAs, downregulation of core gene expression substantially inhibits cell proliferation, revealing a high potential for employing core genes in DCIS prognosis. In conclusion, the current investigation verified the six core genes signatures for prospective DCIS biomarkers, which may aid clinical decision-making for individual care.

Role of Neural (N)-Cadherin in Breast Cancer Cell Stemness and Dormancy in the Bone Microenvironment

Breast cancer cells that interact with spindle-shaped N-Cadherin+ Osteoblasts (SNOs) are recognised to become dormant through a Notch2-dependent mechanism. We found that Notch2High human BrCa MDA-MB231 (MDA) cells also expressed high level of N-Cadherin. This prompted us to hypothesize that N-Cadherin could have a role in MDA-SNO interaction. Of note, the expression of N-Cadherin in MDA cells reduced tumour incidence and bone osteolysis in BrCa mouse model. Moreover, similarly to Notch2High MDA cells, the N-CadherinHigh MDA cells revealed a high expression of the canonical Haematopoietic Stem cell (HSC) markers, suggesting an HSC mimicry, associated with higher ability to form mammospheres. Interestingly, N-CadherinHigh MDA cells showed greater capacity to adhere to SNOs, while the inhibition of SNO-mediating MDA cell proliferation was unremarkable. To investigate whether these features were shared by mouse BrCa, we used the 4T1 cell line in which N-Cadherin expression was abolished and then rescued. At variance with MDA cells, 4T1 cells expressing N-Cadherin revealed that the latter was associated with a lower expression of the HSC marker, Cxcr4, along with a lower capacity to form mammospheres. Furthermore, the rescue of N-Cadherin expression increased cell-cell adhesion and reduced proliferation of 4T1 cells when they were co-plated with SNOs. In conclusion, we demonstrated that: (i) N-CadherinHigh and Notch2High MDA cells showed similar HSC mimicry and dormancy features; (ii) N-Cadherin mediated BrCa-SNO adhesion; (iii) N-Cadherin had a positive Notch2-dependent role on SNO-induced dormancy and HSC mimicry in MDA cells, and a negative role in 4T1 cell stemness and HSC mimicry.

Aspects of the Epigenetic Regulation of EMT Related to Cancer Metastasis

Epithelial to mesenchymal transition (EMT) occurs during the pathological process associated with tumor progression and is considered to influence and promote the metastatic cascade. Characterized by loss of cell adhesion and apex base polarity, EMT enhances cell motility and metastasis. The key markers of the epithelial to mesenchymal transition are proteins characteristic of the epithelial phenotype, e.g., E-cadherin, cytokeratins, occludin, or desmoplakin, the concentration and activity of which are reduced during this process. On the other hand, as a result of acquiring the characteristics of mesenchymal cells, an increased amount of N-cadherin, vimentin, fibronectin, or vitronectin is observed. Importantly, epithelial cells undergo partial EMT where some of the cells show both epithelial and mesenchymal characteristics. The significant influence of epigenetic regulatory mechanisms is observed in the gene expression involved in EMT. Among the epigenetic modifications accompanying incorrect genetic reprogramming in cancer are changes in the level of DNA methylation within the CpG islands and posttranslational covalent changes of histone proteins. All observed modifications, which are stable but reversible changes, affect the level of gene expression leading to the development and progression of the disease, and consequently affect the uncontrolled growth of the population of cancer cells.