Copy number variation, increased gene expression, and molecular mechanisms of neurofascin in lung cancer

CanSeer: a translational methodology for developing personalized cancer models and therapeutics

Computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose "CanSeer"-a methodology for developing personalized therapeutics. CanSeer employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. CanSeer reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc.

One-step diagnosis of infection and lung cancer using metagenomic sequencing

BACKGROUND

Traditional detection methods face challenges in meeting the diverse clinical needs for diagnosing both lung cancer and infections within a single test. Onco-mNGS has emerged as a promising solution capable of accurately identifying infectious pathogens and tumors simultaneously. However, critical evidence is still lacking regarding its diagnostic performance in distinguishing between pulmonary infections, tumors, and non-infectious, non-tumor conditions in real clinical settings.

METHODS

In this study, data were gathered from 223 participants presenting symptoms of lung infection or tumor who underwent Onco-mNGS testing. Patients were categorized into four groups based on clinical diagnoses: infection, tumor, tumor with infection, and non-infection-non-tumor. Comparisons were made across different groups, subtypes, and stages of lung cancer regarding copy number variation (CNV) patterns, microbiome compositions, and clinical detection indices.

RESULTS

Compared to conventional infection testing methods, Onco-mNGS demonstrates superior infection detection performance, boasting a sensitivity of 81.82%, specificity of 72.55%, and an overall accuracy of 77.58%. In lung cancer diagnosis, Onco-mNGS showcases excellent diagnostic capabilities with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value reaching 88.46%, 100%, 91.41%, 100%, and 90.98%, respectively. In bronchoalveolar lavage fluid (BALF) samples, these values stand at 87.5%, 100%, 94.74%, 100%, and 91.67%, respectively. Notably, more abundant CNV mutation types and higher mutation rates were observed in adenocarcinoma (ADC) compared to squamous cell carcinoma (SCC). Concurrently, onco-mNGS data revealed specific enrichment of Capnocytophaga sputigeria in the ADC group and Candida parapsilosis in the SCC group. These species exhibited significant correlations with C reaction protein (CRP) and CA153 values. Furthermore, Haemophilus influenzae was enriched in the early-stage SCC group and significantly associated with CRP values.

CONCLUSIONS

Onco-mNGS has exhibited exceptional efficiencies in the detection and differentiation of infection and lung cancer. This study provides a novel technological option for achieving single-step precise and swift detection of lung cancer.

Evaluation of circulating plasma proteins in prostate cancer using mendelian randomization

BACKGROUND

The proteome is an important resource for exploring potential diagnostic and therapeutic targets for cancer. This study aimed to investigate the causal associations between plasma proteins and prostate cancer (PCa), and to explore the downstream phenotypes that plasma proteins may influence and potential upstream intervening factors.

METHODS

Proteome-wide Mendelian randomization was used to investigate the causal effects of plasma proteins on PCa. Colocalization analysis examined the common causal variants between plasma proteins and PCa. Summary-statistics-based Mendelian Randomization (SMR) analyses identified associations between the expression of protein-coding genes and PCa. Phenome-wide association study was performed to explore the effect of target proteins on downstream phenotypes. Finally, a systematic Mendelian randomization analysis between lifestyle factors and plasma proteins was performed to assess upstream intervening factors for plasma proteins.

RESULTS

The findings revealed a positive genetic association between the predicted plasma levels of nine proteins and an elevated risk of PCa, while four proteins exhibited an inverse association with PCa risk. SMR analyses revealed ZG16B, PEX14 in blood and ZG16B, NAPG in prostate tissue were potential drug targets for PCa. The genetic association of PEX14 with PCa was further supported by colocalization analysis. Further Phenome-wide association study showed possible side effects of ZG16B, PEX14 and NAPG as drug targets. 10 plasma proteins (RBP7, TPST1, NFASC, LAYN, HDGF, SERPIMA5, DLL4, EFNA3, LIMA1, and CCL27) could be modulated by lifestyle-related factors.

CONCLUSION

This study explores the genetic associations between plasma proteins and PCa, provides evidence that plasma proteins serve as potential drug targets and enhances the understanding of the molecular etiology, prevention and treatment of PCa.

XL1R-Net: Explainable AI-driven improved L1-regularized deep neural architecture for NSCLC biomarker identification

BACKGROUND AND OBJECTIVE

Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes - Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data.

METHODS

An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers.

RESULTS

The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug-Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K-M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients' likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List.

CONCLUSION

A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC's genetic diversity, using only one omics data type may not adequately capture the tumor's complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

Integrative Analyses of Pyrimidine Salvage Pathway-Related Genes Revealing the Associations Between UPP1 and Tumor Microenvironment

Background

The pyrimidine salvage pathway plays a critical role in tumor progression and patient outcomes. The roles of pyrimidine salvage pathway-related genes (PSPGs) in cancer, however, are not fully understood. This study aims to depict the characteristics of PSPGs across various cancers.

Methods

An integrative pan-cancer analysis of six PSPGs (CDA, UCK1, UCK2, UCKL1, UPP1, and UPP2) was conducted using TCGA data, single-cell RNA sequencing datasets, and patient samples. Single-cell transcriptome analysis and RT-qPCR were used to validate the relation between UPP1 and cytokines. Flow cytometry was performed to validate the role of UPP1 in immune checkpoint regulation. The correlation between UPP1 and tumor associated neutrophils (TAN) were investigated and validated by single-cell transcriptome analysis and tissue microarrays (TMAs).

Results

PSPGs showed low mutation rates but significant copy number variations, particularly amplifications in UCKL1, UPP1, and UCK2 across various cancers. DNA methylation patterns varied, with notable negative correlations between methylation and gene expression in UPP1. PSPGs were broadly up-regulated in multiple cancers, with correlations to clinical staging and prognosis. Proteomic data further confirmed these findings. Functional analysis revealed PSPGs' associations with tumor proliferation, metastasis, and various signaling pathways. UPP1 showed strong correlations with the tumor microenvironment (TME), particularly with cytokines, immune checkpoints, and various immune cells. Single-cell transcriptome analysis confirmed these associations, highlighting UPP1's influence on cytokine expression and immune checkpoint regulation. In esophageal squamous cell carcinoma (ESCC), UPP1-high tumor cells were significantly associated with immunosuppressive cells in the TME. Spatial analysis using TMAs revealed that UPP1+ tumor cells were predominantly located at the invasive margin and closely associated with neutrophils, correlating with poorer patient prognosis.

Conclusion

Our study depicted the multi-dimensional view of PSPGs in cancer, with a particular focus on UPP1's role in the TME. Targeting UPP1 holds promise as a potential strategy for cancer therapy.

CNV-FB: A Feature bagging strategy-based approach to detect copy number variants from NGS data

Copy number variation (CNV), as a type of genomic structural variation, accounts for a large proportion of structural variation and is related to the pathogenesis and susceptibility to some human diseases, playing an important role in the development and change of human diseases. The development of next-generation sequencing technology (NGS) provides strong support for the design of CNV detection algorithms. Although a large number of methods have been developed to detect CNVs using NGS data, it is still considered a difficult problem to detect CNVs with low purity and coverage. In this paper, a new calculation method CNV-FB is proposed to detect CNVs from NGS data. The core idea of CNV-FB is to randomly sample the read depth values of the genome fragment, and then each sample is individually detected for outliers, and finally combined into a final outlier score. The CNV-FB method was applied to simulation data and real data experiments and compared with the other five methods of the same type. The results show that the CNV-FB method has a better detection effect than other methods. Therefore, the CNV-FB method may be an effective algorithm for detecting genomic mutations.

Risk scoring based on DNA methylation-driven related DEGs for colorectal cancer prognosis with systematic insights

Colorectal cancer is a common malignant tumor of the digestive tract. Despite advances in diagnostic techniques and medications. Its prognosis remains challenging. DNA methylation-driven related circulating tumor cells have attracted enormous interest in diagnosing owing to their non-invasive nature and early recognition properties. However, the mechanism through which risk biomarkers act remains elusive. Here, we designed a risk model based on differentially expressed genes, DNA methylation, robust, and survival-related factors in the framework of Cox regression. The model has satisfactory performance and is independently verified by an external and isolated dataset in terms of C-index value, ROC, and tROC. The model was applied to Colorectal cancer patients who were subsequently divided into high- and low-risk groups. Functional annotations, genomic alterations, tumor immune environment, and drug sensitivity were analyzed. We observed that up-regulated genes are associated with epithelial cell differentiation and MAPK signaling pathways. The down-regulated genes are related to IL-7 signaling and apoptosis-induced DNA fragmentation. Interestingly, the immune system was inhibited in high-risk groups. High-frequency mutation genes tend to co-occur. High-risk score patients are related to copy number amplification events. To address the challenges, we suggested eleven and twenty-one drugs that are sensitive to low- and high-risk patients. Finally, an artificial neural network was provided to evaluate the immunotherapeutic efficiency. Taken together, the findings demonstrated that our risk score model is robust and reliable for evaluating the prognosis with novel diagnostic and treatment targets. It also yields benefits for the treatment and provides unique insights into developing therapeutic strategies.

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.

Identification of NFASC and CHL1 as Two Novel Hub Genes in Endometriosis Using Integrated Bioinformatic Analysis and Experimental Verification

Background

Endometriosis (EMS) is a common and highly recurrent gynecological disease characterized by chronic pain and infertility. There are no definitive therapies for endometriosis since the pathogenesis remains undetermined. This study aimed to identify EMS-related functional modules and hub genes by integrated bioinformatics analysis.

Methods

Three endometriosis expression profiling series (GSE25628, GSE23339, and GSE7305) were obtained from Gene Expression Omnibus (GEO). The EMS-related module was constructed by weighted gene co-expression network analysis (WGCNA), followed by Gene Ontology (GO) enrichment analyses. Cytohubba and the MCODE plug-ins of Cytoscape were used to screen out the hub genes, which were verified via receiver operating characteristic (ROC) curves. Immunohistochemistry was performed to verify the protein expression of the hub genes in ectopic endometrial tissues. Moreover, CIBERSORT was used to analyze the relationship between the abundance of immune cells infiltration and the expression of hub genes.

Results

Among the 18 modules obtained, the darkmagenta module was identified as the EMS-related module, genes of which were significantly enriched to terms referring to cell migration and neurogenesis. NFASC and CHL1 were screened out and prioritized as hub genes through Cytoscape and confirmed to be differentially upregulated in ectopic endometrial samples. Finally, the expression of hub genes was related to the abundance of immune cells infiltration. The higher expression of NFASC or CHL1 correlated with increased M2 macrophages and decreased natural killer (NK) cells in ectopic lesions.

Conclusion

This study provided new insights into the molecular factors underlying the pathogenesis of endometriosis and provided a theoretical basis for the potential that the two hub genes, NFASC and CHL1, might be novel biomarkers and therapeutic targets in the future.

TRIM46 activates AKT/HK2 signaling by modifying PHLPP2 ubiquitylation to promote glycolysis and chemoresistance of lung cancer cells

The incidence of lung cancer is increasing worldwide. Although great progress in lung cancer treatment has been made, the clinical outcome is still unsatisfactory. Tripartite motif (TRIM)-containing proteins has been shown to be closely related to tumor progression. However, the function of TRIM46 in lung cancer is largely unknown. Here, TRIM46 amplification was found in lung adenocarcinoma (LUAD) tissues and TRIM46 amplification was significantly associated with a poor survival rate. Overexpression of wild type TRIM46 increased the proliferation of LUAD cells and glycolysis, promoted xenografts growth, and enhanced cisplatin (DDP) resistance of LUAD cells via increased ubiquitination of pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) and upregulation of p-AKT. In contrast, overexpression of RING-mutant TRIM46 did not show any effects, suggesting the function of TRIM46 was dependent on the E3 ligase activity. Furthermore, we found that TRIM46 promoted LUAD cell proliferation and DDP resistance by enhancing glycolysis. PHLPP2 overexpression reversed the effects of TRIM46 overexpression. Amplification of TRIM46 also promoted LUAD growth and enhanced its DDP resistance in a patient-derived xenograft (PDX) model. In conclusion, our data highlight the importance of TRIM46/PHLPP2/AKT signaling in lung cancer and provide new insights into therapeutic strategies for lung cancer.

STIL Acts as an Oncogenetic Driver in a Primary Cilia-Dependent Manner in Human Cancer

SCL/TAL1 Interrupting locus (STIL) is a ciliary-related gene involved in regulating the cell cycle and duplication of centrioles in dividing cells. STIL has been found disordered in multiple cancers and driven carcinogenesis. However, the molecular mechanisms and biological functions of STIL in cancers remain ambiguous. Here, we systematically analyzed the genetic alterations, molecular mechanisms, and clinical relevance of STIL across >10,000 samples representing 33 cancer types in The Cancer Genome Atlas (TCGA) dataset. We found that STIL expression is up-regulated in most cancer types compared with their adjacent normal tissues. The expression dysregulation of STIL was affected by copy number variation, mutation, and DNA methylation. High STIL expression was associated with worse outcomes and promoted the progression of cancers. Gene Ontology (GO) enrichment analysis and Gene Set Variation Analysis (GSVA) further revealed that STIL is involved in cell cycle progression, Mitotic spindle, G2M checkpoint, and E2F targets pathways across cancer types. STIL expression was negatively correlated with multiple genes taking part in ciliogenesis and was positively correlated with several genes which participated with centrosomal duplication or cilia degradation. Moreover, STIL silencing could promote primary cilia formation and inhibit cell cycle protein expression in prostate and kidney cancer cell lines. The phenotype and protein expression alteration due to STIL silencing could be reversed by IFT88 silencing in cancer cells. These results revealed that STIL could regulate the cell cycle through primary cilia in tumor cells. In summary, our results revealed the importance of STIL in cancers. Targeting STIL might be a novel therapeutic approach for cancers.

Establishment of a novel prognostic signature based on an identified expression profile of integrin superfamily to predict overall survival of patients with colorectal adenocarcinoma

The abnormal expression of integrin superfamily members commonly related to kinds of malignancies. However, the role of integrins in predicting the prognosis of cancers is still little known, especially for colorectal cancer that is one of the leading causes of cancer-related death. RNA-seq data and clinical features of colorectal adenocarcinoma (COAD) patients were derived from The Cancer Genome Atlas (TCGA), used to analyze the expression pattern and genomic alterations of integrin genes in the COAD cohort. Unsupervised hierarchical clustering divided COAD patients into two clusters (clusters 1 & 2), and we observed that patients in cluster 2 with high expressions of most integrin genes had worse clinical features and shorter overall survival (a median OS: 67.25 months vs 99.93 months, p = 0.012), compared to those in cluster 1. Combined with univariate Cox regression analysis, Pearson Correlation Coefficients (PCC), and Principal Component Analysis (PCA), an integrin-related signature was established, including ITGA1, ITGA5, ITGA7, ITGA11, ITGAX, ITGAM, ITGB1, and ITGB5. And the AUC values for OS at 1, 3, and 5 years was 0.61, 0.59, and 0.56, further demonstrating the predicting capacity of our signature. Furthermore, overexpression of which also significantly correlated with poorer prognosis of colon cancer patients in a separate validation cohort, GSE17536 (p < 0.05). Meanwhile, the AUC values for OS in the validation cohort at 1, 3, and 5 years was 0.62, 0.59, and 0.59. Additionally, enrichment analysis indicated significant differences between cluster 1 and cluster 2 in the biological processes of cell adhesion, signal transduction, extracellular matrix, immune system, and in tumor microenvironment (TME), which were crucial to the progression of tumor. The findings supplied compelling evidence that our signature could be a novel prognostic biomarker for COAD patients, and these genes had the potential to be therapeutic targets.

Integrated bioinformatics and experiments reveal the roles and driving forces for HSF1 in colorectal cancer

Heat shock factor 1 (HSF1) has watershed significance in different tumors. However, the roles and driving forces for HSF1 in colorectal cancer (CRC) are poorly understood. Our study integrally analyzed the roles and driving forces for HSF1 in CRC by bioinformatics and experiments. The expression and prognostic characteristics of HSF1 were analyzed via UALCAN, GEPIA2, TISIDB, Prognoscan and HPA databases. Then, we analyzed the correlation between HSF1 expression and immune features via TIMER2 database. Subsequently, we explored the driving forces for HSF1 abnormal expression in CRC by bioinformatics and experiments. Our results showed that HSF1 was overexpressed and correlated with poor prognosis in CRC. And the expression of HSF1 was significantly correlated with multiple immune cell infiltration and was negatively correlated with immunomodulators such as programmed cell death 1 ligand 1(PD-L1). Along with many driver genes in particular TP53, super-enhancer, miRNA and DNA methylation were all responsible for HSF1 overexpression in CRC. Moreover, we demonstrated that β-catenin could promote the translation process of HSF1 mRNA by interacting with HuR, which could directly bind to the coding sequence (CDS) region of HSF1 mRNA. Collectively, HSF1 may be useful as a diagnostic and prognostic biomarker for CRC. HSF1 was closely correlated with immune features. Genetic and epigenetic alterations contributed to HSF1 overexpression in CRC. More importantly, we demonstrated that HSF1 may be regulated at the level of mRNA translation by β-catenin-induced HuR activity.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation) – to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

Tumor-suppressive miR-3650 inhibits tumor metastasis by directly targeting NFASC in hepatocellular carcinoma

In recent years, a growing body of evidence has provided support for the important role of microRNAs (miRNAs) in the progression of human cancers. A recent study showed that a novel miRNA miR-3650 expression was significantly decreased in hepatocellular carcinoma (HCC). However, the precise role of miR-3650 in HCC have remained poorly understood. In this study, we found that miR-3650 expression was frequently decreased in HCC tissues. Low expression of miR-3650 is positively associated with tumor metastasis and poor survival of HCC patients. Forced expression of miR-3650 significantly inhibited the migration and epithelial-mesenchymal transition (EMT) of HCC cells. Through bioinformatic analysis and luciferase assays, we confirmed that neurofascin (NFASC) is a directly target mRNA of miR-3650. Rescue experiment demonstrated that NAFSC overexpression could partially counteracted the inhibitory effect of miR-3650 in HCC metastasis and EMT. In conclusion, our findings are the first time to demonstrate that reduced expression of miR-3650 in HCC was correlated with tumor metastasis and poor survival. MiR-3650 repressed HCC migration and EMT by directly targeting NFASC. Our findings suggested that miR-3650 may serve as a potential prognostic marker and promising application in HCC therapy.

Copy number variation is highly correlated with differential gene expression: a pan-cancer study

BACKGROUND

Cancer is a heterogeneous disease with many genetic variations. Lines of evidence have shown copy number variations (CNVs) of certain genes are involved in development and progression of many cancers through the alterations of their gene expression levels on individual or several cancer types. However, it is not quite clear whether the correlation will be a general phenomenon across multiple cancer types.

METHODS

In this study we applied a bioinformatics approach integrating CNV and differential gene expression mathematically across 1025 cell lines and 9159 patient samples to detect their potential relationship.

RESULTS

Our results showed there is a close correlation between CNV and differential gene expression and the copy number displayed a positive linear influence on gene expression for the majority of genes, indicating that genetic variation generated a direct effect on gene transcriptional level. Another independent dataset is utilized to revalidate the relationship between copy number and expression level. Further analysis show genes with general positive linear influence on gene expression are clustered in certain disease-related pathways, which suggests the involvement of CNV in pathophysiology of diseases.

CONCLUSIONS

This study shows the close correlation between CNV and differential gene expression revealing the qualitative relationship between genetic variation and its downstream effect, especially for oncogenes and tumor suppressor genes. It is of a critical importance to elucidate the relationship between copy number variation and gene expression for prevention, diagnosis and treatment of cancer.

Genomic and regulatory characteristics of significant transcription factors in colorectal cancer metastasis

The dysregulation of transcription factors has an important impact on the oncogenesis and tumor progression. Nonetheless, its functions in colorectal cancer metastasis are still unclear. In this study, four transcription factors (HNF4A, HSF1, MECP2 and RAD21) were demonstrated to be associated with the metastasis of colorectal cancer in both RNA and protein levels. To comprehensively explore the intrinsic mechanisms, we profiled the molecular landscape of these metastasis-related transcription factors from multiple perspectives. In particular, as the crucial factors affecting genome stability, both copy number variation and DNA methylation exerted their strengths on the expression of these transcription factors (except MECP2). Additionally, based on a series of bioinformatics analyses, putative long non-coding RNAs were identified as functional regulators. Besides that, rely on the ATAC-Seq and ChIP-Seq profiles, we detected the target genes regulated by each transcription factor in the active chromatin zones. Finally, we inferred the associations between the target genes by Bayesian networks and identified LMO7 and ARL8A as potential clinical biomarkers. Taken together, our research systematically characterized the regulatory cascades of HNF4A, HSF1, MECP2 and RAD21 in colorectal cancer metastasis.

Copy number variation, increased gene expression, and molecular mechanisms of neurofascin in lung cancer

Metastasis and cell adhesion are key aspects of cancer progression. Neurofascin (NFASC) is a member of the immunoglobulin superfamily of adhesion molecules and, while studies on NFASC are inadequate, other members have been indicated pivotal roles in cancer progression and metastasis. This study aimed at increasing the knowledge on the involvement of adhesion molecules in lung cancer progression by studying the regulation and role of NFASC in non‐small cell lung cancer (NSCLC). Here, copy number variations in the NFASC gene were analyzed in tumor and non‐tumorous lung tissues of 204 NSCLC patients. Frequent gene amplifications (OR = 4.50, 95%CI: 2.27‐8.92, P ≤ 0.001) and increased expression of NFASC (P = 0.034) were identified in tumors of NSCLC patients. Furthermore, molecular mechanisms of NFASC in lung cancer progression were evaluated by investigating the effects of NFASC silencing on cell proliferation, viability, migration, and invasion using siRNA technology in four NSCLC cell lines. Silencing of NFASC did not affect cell proliferation or viability but rather decreased NSCLC cell migration (P ≤ 0.001) and led to morphological changes, rearrangements in the actin cytoskeleton and changes in F‐actin networks in migrating NSCLC cell lines. This study is the first to report frequent copy number gain and increased expression of NFASC in NSCLC. Moreover, these data suggest that NFASC is a novel regulator of NSCLC cell motility and support a role of NFASC in the regulation of NSCLC progression.