Increased expression of P-cadherin is an indicator of poor prognosis in breast cancer: a systematic review and meta-analysis

Identification and validation of genes associated with copper death in oral squamous cell carcinoma based on machine learning and weighted gene co-expression network analysis

OBJECTIVE

To identify copper-induced death-associated hub genes in oral squamous cell carcinoma (OSCC) and understand their functional and biological significance using machine learning and Weighted Gene Co-expression Network Analysis (WGCNA).

METHODS

OSCC transcriptomic data from GEO and TCGA databases were subjected to data integration, batch effect removal, background correction, and quantile normalization to select cuproptosis-associated genes using Spearman's correlation analysis. The 'limma' R package was used to filter differentially expressed genes (DEGs). Core module genes selected using gene co-expression network analysis with R package 'WGCNA' were screened using Support Vector Machine (SVM), LASSO regression, and Random Forest (RF) machine learning algorithms and validated using TCGA database samples. Core gene expression variations between OSCC and adjacent normal tissues were validated using immunohistochemistry. Immune infiltration analysis using package 'CIBERSORT' correlated hub genes with immune cells.

RESULTS

From 19 cuproptosis-related genes (identified from literature), 2382 cuproptosis-related mRNA were obtained through Spearman's correlation analysis; 112 DEGs using 'limma' R package and 32 hub genes using WGCNA were obtained. Hub genes TMPRSS11B, SERPINH1, and CDH3 were identified using machine learning algorithms. TCGA validation showed that TMPRSS11B significantly underexpressed (P < 0.001) but SERPINH1 and CDH3 significantly overexpressed (P < 0.001) in tumor samples. The AUC for TMPRSS11B, SERPINH1, and CDH3 in ROC curve analysis were 78.1%, 95.6%, and 87.5%, respectively.

CONCLUSION

TMPRSS11B, SERPINH1, and CDH3 may be pivotal for OSCC development and progression and potential targets for new therapeutic and predictive strategies. However, their specific functions and mechanisms underlying OSCC remain to be elucidated.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

BACKGROUND

Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.

METHODS

Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).

RESULTS

We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).

CONCLUSION

We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.

SIGNIFICANCE

Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

3D Chromatin Architecture Re-Wiring at the CDH3/CDH1 Loci Contributes to E-Cadherin to P-Cadherin Expression Switch in Gastric Cancer

Cadherins are cell-cell adhesion molecules, fundamental for cell architecture and polarity. E-cadherin to P-cadherin switch can rescue adherens junctions in epithelial tumours. Herein, we disclose a mechanism for E-cadherin to P-cadherin switch in gastric cancers. CDH1 and CDH3 mRNA expression was obtained from 42 gastric tumours' RNA-seq data. CRISPR-Cas9 was used to knock out CDH1 and a putative regulatory element. CDH1-depleted and parental cells were submitted to proteomics and enrichment GO terms analysis; ATAC-seq/4C-seq with a CDH1 promoter viewpoint to assess chromatin accessibility and conformation; and RT-PCR/flow cytometry to assess CDH1/E-cadherin and CDH3/P-cadherin expression. In 42% of gastric tumours analysed, CDH1 to CDH3 switch was observed. CDH1 knockout triggered CDH1/E-cadherin complete loss and CDH3/P-cadherin expression increase at plasma membrane. This switch, likely rescuing adherens junctions, increased cell migration/proliferation, commonly observed in aggressive tumours. E- to P-cadherin switch accompanied increased CDH1 promoter interactions with CDH3-eQTL, absent in normal stomach and parental cells. CDH3-eQTL deletion promotes CDH3/CDH1 reduced expression. These data provide evidence that loss of CDH1/E-cadherin expression alters the CDH3 locus chromatin conformation, allowing a CDH1 promoter interaction with a CDH3-eQTL, and promoting CDH3/P-cadherin expression. These data highlight a novel mechanism triggering E- to P-cadherin switch in gastric cancer.

A Cdh3-β-catenin-laminin signaling axis in a subset of breast tumor leader cells control leader cell polarization and directional collective migration

Carcinoma dissemination can occur when heterogeneous tumor and tumor-stromal cell clusters migrate together via collective migration. Cells at the front lead and direct collective migration, yet how these leader cells form and direct migration are not fully appreciated. From live videos of primary mouse and human breast tumor organoids in a 3D microfluidic system mimicking native breast tumor microenvironment, we developed 3D computational models, which hypothesize that leader cells need to generate high protrusive forces and overcome extracellular matrix (ECM) resistance at the leading edge. From single-cell sequencing analyses, we find that leader cells are heterogeneous and identify and isolate a keratin 14- and cadherin-3-positive subpopulation sufficient to lead collective migration. Cdh3 controls leader cell protrusion dynamics through local production of laminin, which is required for integrin/focal adhesion function. Our findings highlight how a subset of leader cells interact with the microenvironment to direct collective migration.

Breast tumor IGF1R regulates cell adhesion and metastasis: alignment of mouse single cell and human breast cancer transcriptomics

Introduction

The acquisition of a metastatic phenotype is the critical event that determines patient survival from breast cancer. Several receptor tyrosine kinases have functions both in promoting and inhibiting metastasis in breast tumors. Although the insulin-like growth factor 1 receptor (IGF1R) has been considered a target for inhibition in breast cancer, low levels of IGF1R expression are associated with worse overall patient survival.

Methods

To determine how reduced IGF1R impacts tumor phenotype in human breast cancers, we used weighted gene co-expression network analysis (WGCNA) of Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) patient data to identify gene modules associated with low IGF1R expression. We then compared these modules to single cell gene expression analyses and phenotypes of mouse mammary tumors with reduced IGF1R signaling or expression in a tumor model of triple negative breast cancer.

Results

WGCNA from METABRIC data revealed gene modules specific to cell cycle, adhesion, and immune cell signaling that were inversely correlated with IGF1R expression in human breast cancers. Integration of human patient data with single cell sequencing data from mouse tumors revealed similar pathways necessary for promoting metastasis in basal-like mammary tumors with reduced signaling or expression of IGF1R. Functional analyses revealed the basis for the enhanced metastatic phenotype including alterations in E- and P-cadherins.

Discussion

Human breast and mouse mammary tumors with reduced IGF1R are associated with upregulation of several pathways necessary for promoting metastasis supporting the conclusion that IGF1R normally helps maintain a metastasis suppressive tumor microenvironment. We further found that reduced IGF1R signaling in tumor epithelial cells dysregulates cadherin expression resulting in reduced cell adhesion.

Breast cancer prognosis and P-cadherin expression: systematic review and study-level meta-analysis

OBJECTIVE

P-cadherin can act both as a tumour suppressor and an oncogene. The clinical prognostic value of P-cadherin overexpression in breast cancer (BC) remains unclear. We conducted a study-level meta-analysis to determine whether P-cadherin expression can help predict prognosis in BC.

METHODS

A systematic literature search was performed to review eligible studies and clarify the relationship between P-cadherin overexpression and overall survival (OS), disease-free survival (DFS), pathological features, molecular subtypes and molecular phenotypes in BC.

RESULTS

Thirty-one studies including 12 332 patients were included. P-cadherin overexpression was correlated with significantly worse OS (HR=1.77, p<0.00001) and DFS (HR=1.96, p<0.00001) than P-cadherin-negative. P-cadherin overexpression could lead to high histological grade (OR=3.33, p<0.00001) and lymph node metastasis (OR=1.62, p<0.00001). Moreover, P-cadherin overexpression was associated with low odds of the luminal A subtype and high odds of the human epidermal growth factor receptor-2 (HER2)-positive and triple-negative subtypes. P-cadherin expression led to low expression of oestrogen and progesterone receptor (OR=0.37 and OR=0.36, respectively, both p<0.00001) and high expression of HER2 (OR=2.31, p<0.00001), Ki-67 (OR=2.79, p<0.00001), epidermal growth factor receptor (OR=5.85, p<0.00001) and cytokeratin 5/6 (OR=6.79, p<0.00001).

CONCLUSIONS

P-cadherin was found to be associated with invasiveness and metastasis. P-cadherin expression can probably be a useful biomarker for predicting poor survival and may act as an independent prognostic predictor.

Antibody array-based proteomic screening of novel biomarkers in malignant biliary stricture

BACKGROUND

Distinguishing between benign and malignant bile duct strictures has long been a diagnostic challenge in clinical practice.

OBJECTIVE

This study aimed to discover novel biomarkers in bile to improve the diagnostic accuracy of malignant biliary strictures.

METHODS

Bile samples were collected from 6 patients with malignant or benign biliary stricture, respectively. Protein profiles of the bile were analyzed with a semi-quantitative human antibody array of 440 proteins. Then the differential expressed proteins were screened by Venn diagram analysis. Following this, the accuracy of these potential biomarkers for discriminating between malignant and non-malignant biliary strictures was validated in a larger (n= 40) group of patients using lasso analysis.

RESULTS

Twenty proteins were found differentially expressed in malignant versus benign biliary strictures, 6 of which were identified by Venn diagram analysis to be up-regulated regardless of the location of biliary strictures. Among the 6 biomarkers, bile lipocalin-2, P-cadherin, and adipsin showed better diagnostic utility than that of bile CA19-9. Lasso analysis identified that lipocalin-2, P-cadherin and CA19-9 as a group of makers best distinguished malignant from benign strictures.

CONCLUSIONS

Lipocalin-2 and P-cadherin measurements in bile could be clinically useful for the detection of malignant biliary strictures.

Systemic analysis of the expression levels and prognosis of breast cancer-related cadherins

Cadherins form connection between cells, facilitate communication, and serve as essential agents in the progression of multiple cancers. Over 100 cadherins have been identified and they are mainly divided into four groups: classical cadherins (CDHs), protocadherins (PCDHs), desmosomal (DSC), and cadherin-related proteins. Accumulating evidence has indicated that several members of the cadherins are involved in breast cancer development. Nevertheless, the expression profiles and corresponding prognostic outcomes of these breast cancer-related cadherins are yet to be analyzed. Here, we examined the expression levels and prognostic potential of these breast cancer-related cadherins from the specific databases viz. oncomine, gene expression profiling interactive analysis, human protein atlas, UALCAN, Kaplan-Meier Plotter, and cBioPortal. We found that the CDH2/11 levels were higher in breast cancer tissues, compared to healthy breast tissues, whereas with CDH3-5, PCDH8/10, and DSC3, the levels were lower in the former than in the latter. Additionally, for CDH1/6/13/17/23, PCDH7, and FAT4, trancript level alterations between breast cancer and healthy tissues varied across different databases. The CDH1 protein levels were elevated in breast cancer tissues versus healthy breast tissues, whereas the protein levels of CDH3/11 and PCDH8/10 were reduced in breast cancer, compared to healthy breast tissues. For CDH15 and CDH23, the expression levels paralleled tumor stage. Survival analysis, using the Kaplan-Meier Plotter database, demonstrated that elevated CDH1-3 levels correlated with diminished relapse-free survival in breast cancer patients. Alternately, enhanced CDH4-6/15/17/23, PCDH10, DSC3, and FAT4 levels estimated a rise in relapse-free survival of breast cancer patients. These data suggest CDH1-3 to be a promising target for breast cancer precision therapy and CDH4-6/15/17/23, PCDH10, DSC3, and FAT4 to be novel biomarkers for breast cancer prognosis.