ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

Abstract Background Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study. Methods Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells. Results We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes. Conclusion Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.

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Screening and Identifying m6A Regulators as an Independent Prognostic Biomarker in Pancreatic Cancer Based on The Cancer Genome Atlas Database

BioMed Research International ◽

10.1155/2021/5573628 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Bi Lin ◽

Yangyang Pan ◽

Dinglai Yu ◽

Shengjie Dai ◽

Hongwei Sun ◽

...

Keyword(s):

Pancreatic Cancer ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Tumor Therapy ◽

Disease Free Survival ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas ◽

Genome Atlas

Background. Pancreatic cancer is one of the most malignant tumors of the digestive system, and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods. In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas (TCGA) portal. Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Additionally, we validated the prognostic value of m6A regulators in the International Cancer Genome Consortium (ICGC). Results. The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways in the TCGA database. Conclusion. Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

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Identification of five Hub Genes Associated with Development and Prognosis of Lung Adenocarcinoma by Co-Expression Analysis

Current Bioinformatics ◽

10.2174/1574893616666210624141520 ◽

2021 ◽

Vol 16 ◽

Author(s):

Sangsang Chen ◽

Xuqing Zhu ◽

Jing Zheng ◽

Tingting Xu ◽

Yinmin Xu ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Network Analyses ◽

Study Gene Expression

Objective: Non-small cell lung cancer (NSCLC) is one of the most common types of lung cancer, while lung adenocarcinoma (LUAD) is the most common subtype of NSCLC. In this study, we aimed to identify potential markers that are associated with the prognosis and development of LUAD. Methods and results: In this study, gene expression profiles from 594 LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) database, and 2,503 differentially expressed genes (DEGs) were obtained. Secondly, weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression network for these DEGs, and 16 modules were obtained. Among these, the genes related to the most significant module (turquoise) were found to be closely associated with the stage of LUAD. Then, functional annotation revealed that the genes in the turquoise module were mainly enriched in the pathways involved in the cell cycle and meiosis. Seven candidate hub genes were further screened by using WGCNA and protein-protein interaction network analyses. Expression data of the 7 candidate hub genes in different pathological stages in TCGA-LUAD were taken as the training set, while those in the GSE41271 dataset were used as the validation set. As a result, 5 hub genes (KIF11, KIF23, KIF4A, NUSPA1, RRM2) closely related to the pathological stage of LUAD were screened. Finally, survival and tissue expression analyses were performed on the five hub genes. The results suggested that the five hub genes were closely related to the occurrence and prognosis of LUAD. Conclusion: The study identified five hub genes that could be used as important predictors for the prognosis and development of LUAD.

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Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis

10.1101/2021.09.15.460430 ◽

2021 ◽

Author(s):

Lei Gao ◽

Fu Li ◽

Jiao Cai ◽

Jia Liu ◽

Xi Zhang ◽

...

Keyword(s):

Immune Cell ◽

Cox Regression ◽

Cox Model ◽

Expression Profiles ◽

Bioinformatic Analysis ◽

The Cancer Genome Atlas ◽

Clinical Samples ◽

Immune Gene ◽

Hub Genes ◽

Immune Infiltration

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251), GSE12417 (n=163) and GSE37642 (n=137) datasets and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to these hub genes. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.

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Screening and Identifying m6A Regulators in Pancreatic Cancer Based on The Cancer Genome Atlas Database

10.21203/rs.3.rs-96939/v1 ◽

2020 ◽

Author(s):

Bi Lin ◽

Hongwei Sun ◽

Dinglai Yu ◽

Yukai Xiang ◽

Jie Zhang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Tumor Therapy ◽

Disease Free Survival ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas ◽

Genome Atlas

Abstract Background: Pancreatic cancer is one of the most malignant tumors of the digestive system and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods: In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas portal (TCGA). Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Results: The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways. Conclusion: Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

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A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽

10.1186/s12885-021-08902-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chao Yang ◽

Shuoyang Huang ◽

Fengyu Cao ◽

Yongbin Zheng

Keyword(s):

Lipid Metabolism ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Risk Groups ◽

Clinical Parameter ◽

Gene Expression Omnibus ◽

Metabolic Reprogramming ◽

The Cancer Genome Atlas ◽

Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

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90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary

Frontiers in Oncology ◽

10.3389/fonc.2021.722808 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yi Zhang ◽

Lei Xia ◽

Dawei Ma ◽

Jing Wu ◽

Xinyu Xu ◽

...

Keyword(s):

Gene Expression ◽

Malignant Tumors ◽

Expression Profiles ◽

Cancer Of Unknown Primary ◽

The Cancer Genome Atlas ◽

Unknown Primary ◽

Gene Expression Assay ◽

Primary Location ◽

Cancer Genome Atlas ◽

Expression Characteristics

Cancer of unknown primary (CUP), in which metastatic diseases exist without an identifiable primary location, accounts for about 3–5% of all cancer diagnoses. Successful diagnosis and treatment of such patients are difficult. This study aimed to assess the expression characteristics of 90 genes as a method of identifying the primary site from CUP samples. We validated a 90-gene expression assay and explored its potential diagnostic utility in 44 patients at Jiangsu Cancer Hospital. For each specimen, the expression of 90 tumor-specific genes in malignant tumors was analyzed, and similarity scores were obtained. The types of malignant tumors predicted were compared with the reference diagnosis to calculate the accuracy. In addition, we verified the consistency of the expression profiles of the 90 genes in CUP secondary malignancies and metastatic malignancies in The Cancer Genome Atlas. We also reported a detailed description of the next-generation coding sequences for CUP patients. For each clinical medical specimen collected, the type of malignant tumor predicted and analyzed by the 90-gene expression assay was compared with its reference diagnosis, and the overall accuracy was 95.4%. In addition, the 90-gene expression profile generally accurately classified CUP into the cluster of its primary tumor. Sequencing of the exome transcriptome containing 556 high-frequency gene mutation oncogenes was not significantly related to the 90 genes analysis. Our results demonstrate that the expression characteristics of these 90 genes can be used as a powerful tool to accurately identify the primary sites of CUP. In the future, the inclusion of the 90-gene expression assay in pathological diagnosis will help oncologists use precise treatments, thereby improving the care and outcomes of CUP patients.

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Three Immune-Associated Subtypes of Diffuse Glioma Differ in Immune Infiltration, Immune Checkpoint Molecules, and Prognosis

Frontiers in Oncology ◽

10.3389/fonc.2020.586019 ◽

2020 ◽

Vol 10 ◽

Author(s):

Quanwei Zhou ◽

Xuejun Yan ◽

Weidong Liu ◽

Wen Yin ◽

Hongjuan Xu ◽

...

Keyword(s):

Immune Cell ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Immune Checkpoints ◽

Signal Pathways ◽

Diffuse Glioma ◽

Immune Infiltration ◽

Immune Related Genes

Diffuse glioma is one of the most prevalent malignancies of the brain, with high heterogeneity of tumor-infiltrating immune cells. However, immune-associated subtypes of diffuse glioma have not been determined, nor has the effect of different immune-associated subtypes on disease prognosis and immune infiltration of diffuse glioma patients. We retrieved the expression profiles of immune-related genes from The Cancer Genome Atlas (TCGA) (n = 672) and GSE16011 (n = 268) cohorts and used them to identify subtypes of diffuse glioma via Consensus Cluster Plus analysis. We used the limma, clusterProfiler, ESTIMATE, and survival packages of R for differential analysis, functional enrichment, immune and stromal score evaluation respectively in three subtypes, and performed log-rank tests in immune subtypes of diffuse glioma. The immune-associated features of diffuse glioma in the two cohorts were characterized via bioinformatic analyses of the mRNA expression data of immune-related genes. Three subtypes (C1–3) of diffuse glioma were identified from TCGA data, and were verified using the GSE16011 cohort. We then evaluated their immune characteristics and clinical features. Our mRNA profiling analyses indicated that the different subtypes of diffuse glioma presented differential expression profile of specific genes and signal pathways in the TCGA cohort. Patients with subtype C1, who were mostly diagnosed with grade IV glioma, had poorer outcomes than patients with subtype C2 or C3. Subtype C1 was characterized by a higher degree of immune cell infiltration as estimated by GSVA, and more frequent wildtype IDH1. By contrast, subtype C3 included more grade II and IDH1-mutated glioma, and was associated with more infiltration of CD4+T cells. Most subtype C2 had the features between subtypes C1 and C3. Meanwhile, immune checkpoints and their ligand molecules, including PD1/(PD-L1/PDL2), CTLA4/(CD80/CD86), and B7H3/TLT2, were significantly upregulated in subtype C1 and downregulated in subtype C3. In addition, patients with subtype C1 exhibited more frequent gene mutations. Univariate and multivariate Cox regression analyses revealed that diffuse glioma subtype was an effective, independent, and better prognostic factor. Therefore, we established a novel immune-related classification of diffuse glioma, which provides potential immunotherapy targets for diffuse glioma.

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lncRNA and mRNA signature for prognosis prediction of glioblastoma

Future Oncology ◽

10.2217/fon-2019-0538 ◽

2020 ◽

Vol 16 (13) ◽

pp. 837-848 ◽

Cited By ~ 2

Author(s):

Guohong Liu ◽

Yunbao Pan ◽

Yueying Li ◽

Haibo Xu

Keyword(s):

Cox Regression ◽

Expression Profiles ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Normal Brain ◽

Multiple Gene ◽

Cox Regression Analysis ◽

R Language ◽

Cancer Genome Atlas ◽

Novel Biomarkers

Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using ‘edgeR’ package, survival analysis taking count of single or multiple gene expression level using ‘survival’ package, univariate and multivariate Cox regression analysis using Cox function plugged in ‘survival’ package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. ‘GPCR ligand binding’ and ‘Class A/1’ are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.

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Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Journal of Translational Medicine ◽

10.1186/s12967-019-02173-2 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 19

Author(s):

Lei Zhang ◽

Zhe Zhang ◽

Zhenglun Yu

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Single Gene ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Test Series ◽

Cox Regression Analysis ◽

Incidence And Mortality

Abstract Background Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD. Methods Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model. Results We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors. Conclusion Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

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Weighted gene co-expression network analysis of hub genes in lung adenocarcinoma

Evolutionary Bioinformatics ◽

10.1177/11769343211009898 ◽

2021 ◽

Vol 17 ◽

pp. 117693432110098

Author(s):

Xuan Luo ◽

Lei Feng ◽

WenBo Xu ◽

XueJing Bai ◽

MengNa Wu

Keyword(s):

Lung Adenocarcinoma ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Poor Survival ◽

Hub Genes ◽

Regulatory Relationship ◽

Hub Gene ◽

High Incidence ◽

Cancer Genome Atlas ◽

Relationship Of

Lung adenocarcinoma (LUAD) is a tumor with high incidence. This study aimed to identify the central genes of LUAD. LUAD were analyzed by weighted gene co-expression network (WGCNA), and differentially expressed genes (DEGs) were identified. Samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases and included 515 LUAD samples and 347 normal samples. The WGCNA algorithm generated a total of 10 modules. The top 2 modules (MEturquoise and MEblue) with the highest correlation to LUAD were selected. Ten Hub genes (IL6, CDH1, PECAM1, SPP1, THBS1, HGF, SNCA, CDH5, CAV1, and DLC1) were screened in the intersecting genes of DEGs and WGCNA (MEturquoise and MEblue). Only SPP1 was correlated with LUAD poor survival, indicating that SPP1 may be a key Hub gene for LUAD. The competing endogenous RNA (ceRNA) network was constructed to analyze the regulatory relationship of Hub genes, and SPP1 may be directly regulated by 4 microRNAs (miRNAs) and indirectly regulated by 49 long noncoding RNAs (lncRNAs).

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