High expression of collagen 1A2 promotes the proliferation and metastasis of esophageal cancer cells

CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes

BACKGROUND

There exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data.

RESULTS

In this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential 'dark genes' validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states.

CONCLUSIONS

In conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.

Molecular and immune characterization of Chinese early-stage non-squamous non-small cell lung cancer: a multi-omics cohort study

Background

Albeit considered with superior survival, around 30% of the early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) patients relapse within 5 years, suggesting unique biology. However, the biological characteristics of early-stage Ns-NSCLC, especially in the Chinese population, are still unclear.

Methods

Multi-omics interrogation of early-stage Ns-NSCLC (stage I-III), paired blood samples and normal lung tissues (n=76) by whole-exome sequencing (WES), RNA sequencing, and T-cell receptor (TCR) sequencing were conducted.

Results

An average of 128 exonic mutations were identified, and the most frequently mutant gene was EGFR (55%), followed by TP53 (37%) and TTN (26%). Mutations in MUC17, ABCA2, PDE4DIP, and MYO18B predicted significantly unfavorable disease-free survival (DFS). Moreover, cytobands amplifications in 8q24.3, 14q13.1, 14q11.2, and deletion in 3p21.1 were highlighted in recurrent cases. Higher incidence of human leukocyte antigen loss of heterozygosity (HLA-LOH), higher tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were identified in ever-smokers than never-smokers. HLA-LOH also correlated with higher TMB, TNB, intratumoral heterogeneity (ITH), and whole chromosomal instability (wCIN) scores. Interestingly, higher ITH was an independent predictor of better DFS in early-stage Ns-NSCLC. Up-regulation of immune-related genes, including CRABP2, ULBP2, IL31RA, and IL1A, independently portended a dismal prognosis. Enhanced TCR diversity of peripheral blood mononuclear cells (PBMCs) predicted better prognosis, indicative of a noninvasive method for relapse surveillance. Eventually, seven machine-learning (ML) algorithms were employed to evaluate the predictive accuracy of clinical, genomic, transcriptomic, and TCR repertoire data on DFS, showing that clinical and RNA features combination in the random forest (RF) algorithm, with area under the curve (AUC) of 97.5% and 83.3% in the training and testing cohort, respectively, significantly outperformed other methods.

Conclusions

This study comprehensively profiled the genomic, transcriptomic, and TCR repertoire spectrums of Chinese early-stage Ns-NSCLC, shedding light on biological underpinnings and candidate biomarkers for prognosis development.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

Enhancement of Tumorigenicity, Spheroid Niche, and Drug Resistance of Pancreatic Cancer Cells in Three-Dimensional Culture System

The three-dimensional (3D) cell culture technique has been applied comprehensively as a variable platform for medical research, biochemical signal pathway analysis, and evaluation of anti-tumor treatment response due to an excellent recapitulation of a tumor microenvironment (TME) in the in vitro cultured cancer cells. Pancreatic cancer (PaC) is one of the toughest malignancies with a complex TME and refractory treatment response. To comprehensively study the TME of PaC, there is an eager need to develop a 3D culture model to decompose the cellular components and their cross interactions. Herein, we establish a 3D PaC culture system with cancer stem cell (CSC) and scalability properties. To validate our model, we tested the individual PaC cell and the combined effects with cancer-associated fibroblasts (CAFs) on cancer tumorigenicity, the cellular interaction through the CXCR3/CXCL10 axis, and cellular responses reflection of anti-cancer treatments. With the help of our 3D technology, a simulated malignant spheroid with important stromal populations and TME physiochemical properties may be successfully recreated. It can be used in a wide range of preclinical research and helpful in advancing basic and translational cancer biology.

Gene profiles and mutations in the development of cataracts in the ICR rat model of hereditary cataracts

Cataracts are opacifications of the lens that cause loss of visual acuity and ultimately of eyesight. Age-related cataract develops in most elderly people, but the mechanisms of cataract onset are incompletely understood. The Ihara Cataract Rat (ICR) is an animal model of hereditary cataracts showing cortical opacity that commonly develops prematurely. We identified putative mechanisms of cataract onset in the ICR rat model by measuring gene expression changes before and after cortical cataract development and conducting point mutation analysis. Genes differentially expressed between 4-week-old animals without cortical cataracts and 8-10-week-old animals with cortical cataracts were selected from microarray analysis. Three connections were identified by STRING analysis: (i) Epithelial-Mesenchymal Transition (EMT), including Col1a2, and Pik3r1. (ii) Lens homeostasis, including Aqp5, and Cpm. (iii) Lipid metabolism, including Scd1, Srebf1, and Pnpla3. Subsequently, mutation points were selected by comparing ICR rats with 12 different rats that do not develop cataracts. The apolipoprotein Apoc3 was mutated in ICR rats. Analyses of gene expression changes and point and mutations suggested that abnormalities in EMT or lipid metabolism could contribute to cataract development in ICR rats.

The tight junction protein claudin 6 is a potential target for patient-individualized treatment in esophageal and gastric adenocarcinoma and is associated with poor prognosis

BACKGROUND

The prognosis of esophageal adenocarcinoma (EAC) and gastric adenocarcinoma (GAC) remains poor, and new therapeutic approaches are urgently needed. Claudin 6 (CLDN6) is an oncofetal antigen that is largely absent in healthy tissues and upregulated in several cancers, making it a promising therapeutical target. In this study, the expression of CLDN6 was assessed in an large Caucasian EAC and GAC cohort.

METHODS

RNA-Seq data from 89 EACs and 371 GACs were obtained from The Cancer Genome Atlas project and EAC/GAC cases were stratified by CLDN6 mRNA expression based on a survival-associated cutoff. For groups with CLDN6 expression above or below this cutoff, differential gene expression analyses were performed using DESeq, and dysregulated biological pathways were identified using the Enrichr tool. Additionally, CLDN6 protein expression was assessed in more than 800 EACs and almost 600 GACs using a CLDN6-specific immunohistochemical antibody (clone 58-4B-2) that is currently used in Phase I/II trials to identify patients with CLDN6-positive tumors (NCT05262530; NCT04503278). The expression of CLDN6 was also correlated with histopathological parameters and overall survival (OS).

RESULTS

EACs and GACs with high CLDN6 mRNA levels displayed an overexpression of pathways regulating the cell cycle, DNA replication, and receptor / extracellular matrix interactions. CLDN6 protein expression was associated with shorter OS in EAC and GAC, both in treatment-naïve subgroups and cohorts receiving neoadjuvant therapy. In multivariate analysis, CLDN6 protein expression was an independent adverse prognostic factor in EAC associated with a shorter OS (HR: 1.75; p = 0.01) and GAC (HR: 2.74; p = 0.028).

CONCLUSIONS

High expression of CLDN6 mRNA is associated with the dysregulation of distinct biological pathways regulating cell growth, proliferation, and cell-matrix interactions. Clinically, the expression of CLDN6 protein is a valuable adverse prognostic marker in EAC and GAC.

Identification of gene profiles related to the development of oral cancer using a deep learning technique

BACKGROUND

Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning.

METHODS

Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes.

RESULTS

Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes.

CONCLUSIONS

Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

P4HA1, transcriptionally activated by STAT1, promotes esophageal cancer progression

Esophageal cancer (EC) is one of the most frequent cancers with a higher mortality worldwide. Although prolyl 4-hydroxylase alpha polypeptide I (P4HA1) is involved in various human malignancies, the function of P4HA1 in EC remains unclear. The mRNA and protein expressions were assessed by quantitative real-time polymerase chain reaction, western blot and immunohistochemistry. CCK8 assay was used to detect EC cell viability. Cell proliferation was analyzed by colony formation and ethynyl-2'-deoxyuridine assays. In addition, flow cytometry and TdT-mediated dUTP nick-end labeling staining were performed to detect cell apoptosis. Masson's trichrome staining was used to assess the collagen fiber level in tumor tissues. The interaction between STAT1 and P4HA4 was analyzed using ChIP, dual-luciferase reporter gene and Y1H assays. P4HA1 was overexpressed in EC, and its knockdown suppressed EC cell proliferation and collagen synthesis and increased cell apoptosis. Meanwhile, P4HA1 knockdown could repress EC tumor growth in vivo. Our further research displayed that STAT1 promoted P4HA1 expression by interacting with P4HA1 promoter. As expected, P4HA1 overexpression abolished STAT1 knockdown's repression on EC cell malignant behaviors. Our research proved that P4HA1 was transcriptionally activated by STAT1, thereby promoting EC progression.

Revisiting laminin and extracellular matrix remodeling in metastatic squamous cell carcinoma: What have we learned after more than four decades of research?

Patients with squamous cell carcinoma (SCC) have significantly lower survival upon the development of distant metastases. The extracellular matrix (ECM) is a consistent yet dynamic influence on the metastatic capacity of SCCs. The ECM encompasses a milieu of structural proteins, signaling molecules, and enzymes. Just over 40 years ago, the fibrous ECM glycoprotein laminin was identified. Roughly four decades of research have revealed a pivotal role of laminins in metastasis. However, trends in ECM alterations in some cancers have been applied broadly to all metastatic diseases, despite evidence that these characteristics vary by tumor type. We will summarize how laminins influence the SCC metastatic process exclusively. Enhanced laminin protein deposition occurs at the invasive edge of SCC tumors, which correlates with elevated levels of laminin-binding β1 integrins on SCC cells, increased MMP-3 presence, worse prognosis, and lymphatic dissemination. Although these findings are significant, gaps in knowledge of the formation of a premetastatic niche, the processes of intra- and extravasation, and the contributions of the ECM to SCC metastatic cell dormancy persist. Bridging these gaps requires novel in vitro systems and animal models that reproduce tumor-stromal interactions and spontaneous metastasis seen in the clinic. These advances will allow accurate assessment of laminins to predict responders to transforming growth factor-β inhibitors and immunotherapy, as well as potential combinatorial therapies with the standard of care. Such clinical interventions may drastically improve quality of life and patient survival by explicitly targeting SCC metastasis.

Collagen Family as Promising Biomarkers and Therapeutic Targets in Cancer

Despite advances in cancer detection and therapy, it has been estimated that the incidence of cancers will increase, while the mortality rate will continue to remain high, a fact explained by the large number of patients diagnosed in advanced stages when therapy is often useless. Therefore, it is necessary to invest knowledge and resources in the development of new non-invasive biomarkers for the early detection of cancer and new therapeutic targets for better health management. In this review, we provided an overview on the collagen family as promising biomarkers and on how they may be exploited as therapeutic targets in cancer. The collagen family tridimensional structure, organization, and functions are very complex, being in a tight relationship with the extracellular matrix, tumor, and immune microenvironment. Moreover, accumulating evidence underlines the role of collagens in promoting tumor growth and creating a permissive tumor microenvironment for metastatic dissemination. Knowledge of the molecular basis of these interactions may help in cancer diagnosis and prognosis, in overcoming chemoresistance, and in providing new targets for cancer therapies.

The single cell transcriptional landscape of esophageal adenocarcinoma and its modulation by neoadjuvant chemotherapy

Immune checkpoint blockade has recently proven effective in subsets of patients with esophageal adenocarcinoma (EAC) but little is known regarding the EAC immune microenvironment. We determined the single cell transcriptional profile of EAC in 8 patients who were treatment-naive (n = 4) or had received neoadjuvant chemotherapy (n = 4). Analysis of 52,387 cells revealed 10 major cell subsets of tumor, immune and stromal cells. Prior to chemotherapy tumors were heavy infiltrated by T regulatory cells and exhausted effector T cells whilst plasmacytoid dendritic cells were markedly expanded. Two dominant cancer-associated fibroblast populations were also observed whilst endothelial populations were suppressed. Pathological remission following chemotherapy associated with broad reversal of immune abnormalities together with fibroblast transition and an increase in endothelial cells whilst a chemoresistant epithelial stem cell population correlated with poor response. These findings reveal features that underlie and limit the response to current immunotherapy and identify a range of novel opportunities for targeted therapy.

TGF-β2-induced circ-PRDM5 regulates migration, invasion, and EMT through the miR-92b-3p/COL1A2 pathway in human lens epithelial cells

CircRNA circ-PRDM5 (PR/SET domain 5) (circ-PRDM5) is overexpressed in age-related cataracts. Nevertheless, the biological role of circ-PRDM5 in posterior capsule opacities (PCO) (a common complication after cataract surgery) is unclear. Human lens epithelial cells SRA01/04 (LECs) were stimulated with TGF-β2 (transforming growth factor beta-2) to mimic the PCO model in vitro. Cell viability, migration, and invasion were determined by MTT, transwell, or wound-healing assays. Protein levels of EMT (epithelial-to-mesenchymal transition) markers and COL1A2 (collagen type I alpha 2 chain) were analyzed by western blotting (WB). Relative expression of circ-PRDM5, miR-92b-3p, and COL1A2 mRNA was analyzed by qRT-PCR. The targeting relationship was confirmed by dual-luciferase reporter and RIP assays. We observed that circ-PRDM5 and COL1A2 were upregulated in PCO tissues and TGF-β2-treated LECs, while miR-92b-3p was downregulated. Both circ-PRDM5 and COL1A2 knockdown impaired TGF-β2-induced LEC migration, invasion, and EMT. Also, circ-PRDM5 could adsorb miR-92b-3p to regulate COL1A2 expression. Furthermore, miR-92b-3p inhibitor offset circ-PRDM5 knockdown-mediated influence on migration, invasion, and EMT of LECs under TGF-β2 stimulation. Also, COL1A2 overexpression overturned the repressive influence of miR-92b-3p mimic on TGF-β2-induced LEC migration, invasion, and EMT. In summary, TGF-β2-induced circ-PRDM5 facilitated LEC migration, invasion, and EMT by adsorbing miR-92b-3p and increasing COL1A2 expression, offering new insights into the development of PCO.

Barrett's esophagus: The pathomorphological and molecular genetic keystones of neoplastic progression

Barrett's esophagus is a widespread chronically progressing disease of heterogeneous nature. A life threatening complication of this condition is neoplastic transformation, which is often overlooked due to lack of standardized approaches in diagnosis, preventative measures and treatment. In this essay, we aim to stratify existing data to show specific associations between neoplastic transformation and the underlying processes which predate cancerous transition. We discuss pathomorphological, genetic, epigenetic, molecular and immunohistochemical methods related to neoplasia detection on the basis of Barrett's esophagus. Our review sheds light on pathways of such neoplastic progression in the distal esophagus, providing valuable insight into progression assessment, preventative targets and treatment modalities. Our results suggest that molecular, genetic and epigenetic alterations in the esophagus arise earlier than cancerous transformation, meaning the discussed targets can help form preventative strategies in at-risk patient groups.

Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis

Independent Component Analysis is a matrix factorization method for data dimension reduction. ICA has been widely applied for the analysis of transcriptomic data for blind separation of biological, environmental, and technical factors affecting gene expression. The study aimed to analyze the publicly available esophageal cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways and molecular signatures involved in this cancer type. In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used. A bioinformatics tool « BiODICA-Independent Component Analysis of Big Omics Data» was applied to compute independent components (ICs). Gene Set Enrichment Analysis (GSEA) and ToppGene uncovered the most significantly enriched pathways. Construction and visualization of gene networks and graphs were performed using the Cytoscape, and HPRD database. The correlation graph between decompositions into 30 ICs was built with absolute correlation values exceeding 0.3. Clusters of components-pseudocliques were observed in the structure of the correlation graph. The top 1,000 most contributing genes of each ICs in the pseudocliques were mapped to the PPI network to construct associated signaling pathways. Some cliques were composed of densely interconnected nodes and included components common to most cancer types (such as cell cycle and extracellular matrix signals), while others were specific to EC. The results of this investigation may reveal potential biomarkers of esophageal carcinogenesis, functional subsystems dysregulated in the tumor cells, and be helpful in predicting the early development of a tumor.

ECM Remodeling in Squamous Cell Carcinoma of the Aerodigestive Tract: Pathways for Cancer Dissemination and Emerging Biomarkers

Squamous cell carcinomas (SCC) include a number of different types of tumors developing in the skin, in hollow organs, as well as the upper aerodigestive tract (UADT) including the head and neck region and the esophagus which will be dealt with in this review. These tumors are often refractory to current therapeutic approaches with poor patient outcome. The most important prognostic determinant of SCC tumors is the presence of distant metastasis, significantly correlating with low patient survival rates. Rapidly emerging evidence indicate that the extracellular matrix (ECM) composition and remodeling profoundly affect SSC metastatic dissemination. In this review, we will summarize the current knowledge on the role of ECM and its remodeling enzymes in affecting the growth and dissemination of UADT SCC. Taken together, these published evidence suggest that a thorough analysis of the ECM composition in the UADT SCC microenvironment may help disclosing the mechanism of resistance to the treatments and help defining possible targets for clinical intervention.

ECM Remodeling in Squamous Cell Carcinoma of the Aerodigestive Tract: Pathways for Cancer Dissemination and Emerging Biomarkers

Simple Summary

Local and distant metastasis of patients affected by squamous cell carcinoma of the upper aerodigestive tract predicts poor prognosis. In the latest years, the introduction of new therapeutic approaches, including targeted and immune therapies, has improved the overall survival. However, a large number of these patients do not benefit from these treatments. Thus, the identification of suitable prognostic and predictive biomarkers, as well as the discovery of new therapeutic targets have emerged as a crucial clinical need. In this context, the extracellular matrix represents a suitable target for the development of such therapeutic tools. In fact, the extracellular matrix is composed by complex molecules able to interact with a plethora of receptors and growth factors, thus modulating the dynamic crosstalk between cancer cells and the tumor microenvironment. In this review, we summarize the current knowledge of the role of the extracellular matrix in affecting squamous cell carcinoma growth and dissemination. Despite extracellular matrix is known to affect the development of many cancer types, only a restricted number of these molecules have been recognized to impact on squamous cell carcinoma progression. Thus, we consider that a thorough analysis of these molecules may be key to develop new potential therapeutic targets/biomarkers.

Abstract

Squamous cell carcinomas (SCC) include a number of different types of tumors developing in the skin, in hollow organs, as well as the upper aerodigestive tract (UADT) including the head and neck region and the esophagus which will be dealt with in this review. These tumors are often refractory to current therapeutic approaches with poor patient outcome. The most important prognostic determinant of SCC tumors is the presence of distant metastasis, significantly correlating with low patient survival rates. Rapidly emerging evidence indicate that the extracellular matrix (ECM) composition and remodeling profoundly affect SSC metastatic dissemination. In this review, we will summarize the current knowledge on the role of ECM and its remodeling enzymes in affecting the growth and dissemination of UADT SCC. Taken together, these published evidence suggest that a thorough analysis of the ECM composition in the UADT SCC microenvironment may help disclosing the mechanism of resistance to the treatments and help defining possible targets for clinical intervention.