Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

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COPB2: A Novel Prognostic Biomarker That Affects Progression of HCC

BioMed Research International ◽

10.1155/2021/6648078 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Jiayao Zhang ◽

Xiaoyu Wang ◽

Guangbing Li ◽

Jingyi He ◽

Ziwen Lu ◽

...

Keyword(s):

Survival Analysis ◽

Epithelial Mesenchymal Transition ◽

Prognostic Biomarker ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Migration And Invasion ◽

Clinicopathologic Characteristics ◽

Mesenchymal Transition ◽

Kaplan Meier

Purpose. This study is aimed at investigating the expression, underlying biological function, and clinical significance of coatomer protein complex subunit beta 2 (COPB2) in hepatocellular carcinoma (HCC). Methods. HCC-related data were extracted from The Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and Gene Expression Omnibus (GEO) database. A logistic regression module was applied to analyze the relationship between the expression of COPB2 and clinicopathologic characteristics. The Cox proportional hazard regression model and Kaplan–Meier method were used for survival analysis. Gene set enrichment analysis (GSEA) was used to annotate the underlying biological functions. Loss-of-function experiments were conducted to determine the underlying mechanisms. Results. COPB2 was overexpressed in HCC, and high expression of COPB2 was significantly correlated with higher alpha fetoprotein (AFP) (odds ratio OR = 1.616 , >20 vs. ≤20, p < 0.05 ), stage ( OR = 1.744 , III vs. I, p < 0.05 ), and grade ( OR = 1.746 , G4+G3 vs. G2+G1, p < 0.05 ). Kaplan–Meier survival analysis showed that HCC patients with high COPB2 expression had a worse prognosis than those with low COPB2 expression ( p < 0.0001 for TCGA cohort, p < 0.05 for ICGC cohort). The univariate Cox (hazard ratio HR = 1.068 , p < 0.0001 ) and multivariate Cox ( HR = 2.011 , p < 0.05 ) regression analyses suggested that COPB2 was an independent risk factor. GSEA showed that mTOR and other tumor-related signaling pathways were differentially enriched in the high COPB2 expression phenotype. Silencing of COPB2 inhibited the proliferation, migration, and invasion abilities by suppressing epithelial-mesenchymal transition and mTOR signaling. Conclusion. COPB2 is a novel prognostic biomarker and a promising therapeutic target for HCC.

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A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02329-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wanting Song ◽

Yi Bai ◽

Jialin Zhu ◽

Fanxin Zeng ◽

Chunmeng Yang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Critical Function ◽

Mesenchymal Transition ◽

Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

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Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.679133 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Xiao ◽

Liyan Liu ◽

Zhuoyuan Chen ◽

Aoyu Li ◽

Pingxiao Wang ◽

...

Keyword(s):

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Roc Curves ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

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Prognostic phenotypes of early-stage lung adenocarcinoma

European Respiratory Journal ◽

10.1183/13993003.01674-2021 ◽

2021 ◽

pp. 2101674

Author(s):

Anne-Sophie Lamort ◽

Jan Christian Kaiser ◽

Mario A.A. Pepe ◽

Ioannis Lilis ◽

Giannoula Ntaliarda ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Early Stage ◽

Machine Learning Algorithms ◽

Prognostic Information ◽

Tissue Samples ◽

Cancer Hallmarks ◽

Patient Subgroups ◽

Prognostic Power

BackgroundSurvival after curative resection of early-stage lung adenocarcinoma (LUAD) varies and prognostic biomarkers are urgently needed.MethodsLarge-format tissue samples from a prospective cohort of 200 patients with resected LUAD were immunophenotyped for cancer hallmarks TP53, NF1, CD45, PD-1, PCNA, TUNEL, and FVIII, and were followed for median (95%CI)=2.34 (1.71–3.49) years.ResultsUnsupervised hierarchical clustering revealed two patient subgroups with similar clinicopathologic features and genotype, but with markedly different survival: “proliferative” patients (60%) with elevated TP53, NF1, CD45, and PCNA expression had 50% 5-year overall survival while “apoptotic” patients (40%) with high TUNEL had 70% 5-year survival [HR95%CI=2.23 (1.33–3.80); p=0.0069]. Cox regression and machine learning algorithms including random forests built clinically useful models: a score to predict overall survival and a formula and nomogram to predict tumour phenotype. The distinct LUAD phenotypes were validated in TCGA and KMplotter data and showed prognostic power supplementary to IASLC TNM stage and WHO histologic classification.ConclusionsTwo molecular subtypes of LUAD exist and their identification provides important prognostic information.

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A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.741112 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jun Wu ◽

Yuqing Lou ◽

Yi-Min Ma ◽

Jun Xu ◽

Tieliu Shi

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Early Stage ◽

Differential Expression Analysis ◽

The Cancer Genome Atlas ◽

Clinical Staging ◽

Risk Scores ◽

Regulatory Relationship ◽

Score Model

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e−16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e−4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

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BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2022/3921095 ◽

2022 ◽

Vol 2022 ◽

pp. 1-16

Author(s):

Jin Zhou ◽

Zheming Liu ◽

Huibo Zhang ◽

Tianyu Lei ◽

Jiahui Liu ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Early Stage ◽

External Validation ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

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Systematic Analyses of a Chemokine Family-Based Risk Model Predicting Clinical Outcome and Immunotherapy Response in Lung Adenocarcinoma

Cell Transplantation ◽

10.1177/09636897211055046 ◽

2021 ◽

Vol 30 ◽

pp. 096368972110550

Author(s):

Jiarui Chen ◽

Xingyu Liu ◽

Qiuji Wu ◽

Xueping Jiang ◽

Zihang Zeng ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Model ◽

Cox Regression ◽

Expression Patterns ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Family Based ◽

Chemokine Family

Chemokines exhibited complicated functions in antitumor immunity, with their expression profile and clinical importance of lung adenocarcinoma (LUAD) patients remaining largely undetermined. This study aimed to explore the expression patterns of chemokine family in LUAD and construct a predictive chemokine family-based signature. A total of 497 samples were downloaded from the Cancer Genome Atlas (TCGA) data portal as the training set, and the combination of 4 representative Gene Expression Omnibus (GEO) datasets, including GSE30219, GSE50081, GSE37745, and GSE31210, were utilized as the validation set. A three gene-based signature was constructed using univariate and stepwise multivariate Cox regression analysis, classifying patients into high and low risk groups according to the overall survival. The independent GEO datasets were utilized to validate this signature. Another multivariate analysis revealed that this signature remained an independent prognostic factor in LUAD patients. Furthermore, patients in the low risk group featured immunoactive tumor microenvironment (TME), higher IPS scores and lower TIDE scores, and was regarded as the potential beneficiaries of immunotherapy. Finally, the role of risky CCL20 was validated by immunohistochemistry (IHC), and patients possessed higher CCL20 expression presented shorter overall survival ( P = 0.011).

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A Novel E3-ubiquitin Ligase-Related LncRNA Signature for Predicting Prognosis in Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-1216648/v1 ◽

2022 ◽

Author(s):

Tao Liu ◽

Lan Chen ◽

Xiu-Yi Huang ◽

Shuang Dai ◽

Tao Ren ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Ubiquitin Ligase ◽

Predictive Value ◽

Cox Regression ◽

E3 Ubiquitin Ligase ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Multiple Tumor

Abstract Background: E3 ubiquitin ligase mRNA plays an important role in mediating tumor microenvironment, and is involved in tumor initiation and progression. However, few studies have realized the value of E3 ubiquitin ligase-related lncRNAs in lung adenocarcinoma (LUAD).Methods: Herein, we comprehensively evaluated the E3-ubiquitination patterns including multiple tumor-related molecular phenotypes in LUAD samples using lncRNA profiling from GEO and TCGA database, identified a survival-related risk signature consisting of E3-ubiquitin ligase-related lncRNAs via LASSO and multivariate stepwise Cox regression analysis. Based on the risk score calculated for each sample, LUAD patients were divided into high- and low-risk groups. The predictive value of the signature in overall survival was explored, and a nomogram integrating the risk signature and clinical characteristics was identified and tested. Results: A risk signature consisting of 7 specific E3-ubiquitin ligase-related lncRNAs was screened, and can be viewed as a reliable independent predictor of prognosis. We performed consensus clustering analysis and successfully identified 4 molecular subtypes significantly linked to the OS of LUAD, which validates the prognostic and predictive value of this signature to some extent. The ssGESA analysis revealed that the high-risk group was bound up closely with epithelial-mesenchymal transition, hypoxia, and PI3K/AKT/mTOR pathways, and had a worse outcome. Moreover, we created a nomogram consisting of pathological staging and risk score. Validation analysis demonstrated high conformity of nomogram predictive probability and actual overall survival in LUAD of TCGA and GEO datasets.Conclusion: The model consisting of specific E3-ubiquitin ligase-related lncRNAs contributes to predicting the prognosis of LUAD patients.

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Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

BMC Cancer ◽

10.1186/s12885-020-07615-5 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Zizhen Zhang ◽

Sheng Zheng ◽

Yifeng Lin ◽

Jiawei Sun ◽

Ning Ding ◽

...

Keyword(s):

Colorectal Cancer ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Related Gene ◽

Mesenchymal Transition ◽

Cox Regression Analysis

Abstract Background The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also plays an important role in cancer invasion, metastasis, and chemoresistance. Additionally, EMT is partially responsible for chemoresistance in colorectal cancer (CRC). The aim of this research is to develop an EMT-based prognostic signature in CRC. Methods RNA-seq and microarray data, together with clinical information, were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A total of 244 differentially expressed EMT-related genes (ERGs) were obtained by comparing the expression between normal and tumor tissues. An EMT-related signature of 11 genes was identified as crucially related to the overall survival (OS) of patients through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and Cox regression analysis. Finally, we established a clinical nomogram to predict the survival possibility of CRC patients by integrating clinical characteristics and the EMT-related gene signature. Results Two hundred and forty-four differentially expressed ERGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that EMT-related signaling pathway genes were highly related to CRC. Kaplan-Meier analysis revealed that the 11-EMT signature could significantly distinguish high- and low-risk patients in both TCGA and GEO CRC cohorts. In addition, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusion We developed a novel EMT-related gene signature for the prognosis prediction of CRC patients, which could improve the individualized outcome prediction in CRC.

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KRT6A Promotes EMT and Cancer Stem Cell Transformation in Lung Adenocarcinoma

Technology in Cancer Research & Treatment ◽

10.1177/1533033820921248 ◽

2020 ◽

Vol 19 ◽

pp. 153303382092124

Author(s):

Bin Yang ◽

Wei Zhang ◽

Mengmeng Zhang ◽

Xuhong Wang ◽

Shengzu Peng ◽

...

Keyword(s):

Lung Cancer ◽

Stem Cells ◽

Cancer Stem Cells ◽

Lung Adenocarcinoma ◽

Biological Function ◽

Epithelial Mesenchymal Transition ◽

The Cancer Genome Atlas ◽

Normal Lung ◽

Mesenchymal Transition

Aim: Keratin 6A is a type II cytokeratin which is important in forming nail bed, filiform papillae, the epithelial lining of oral mucosa, and esophagus; recently, keratin 6A was found hyperexpressed in different types of cancer. But, the biological function of keratin 6A in lung adenocarcinoma still remains unclear. Therefore, in current study, we investigated the biological role of keratin 6A in lung adenocarcinoma. Methods: By utilizing The Cancer Genome Atlas database, we investigated the expression profile of keratin 6A and its relationship with other clinical parameters in lung adenocarcinoma. The biological function of keratin 6A in lung adenocarcinoma was also investigated by using A549 and PC-9 lung cancer cell lines in vitro. Results: Our data indicate that, compared with normal lung tissue samples, keratin 6A was hyperexpressed in lung adenocarcinoma. Moreover, keratin 6A hyperexpression was positively correlated with lymph node positive and aggressive tumor T stage. Keratin 6A knockdown inhibited the cell proliferation, migration, and colony formation ability but not cell death in lung adenocarcinoma cells. In addition, we found keratin 6A exerted its phenotype via promoting cancer stem cells (CXCR4high/CD133high) transformation and epithelial–mesenchymal transition. Conclusion: In conclusion, current study suggests that hyperexpressed keratin 6A in lung adenocarcinoma promotes lung cancer proliferation and metastasis via epithelial–mesenchymal transition and cancer stem cells transformation.

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