4.6 Article

Validating HMMR Expression and Its Prognostic Significance in Lung Adenocarcinoma Based on Data Mining and Bioinformatics Methods

Journal

FRONTIERS IN ONCOLOGY
Volume 11, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2021.720302

Keywords

lung adenocarcinoma; HMMR; TCGA; bioinformatics; analysis; GSEA; PPI; prognosis

Categories

Funding

  1. Talented Scientific Research Foundation of Xuzhou Medical University [D2018018]

Ask authors/readers for more resources

In lung adenocarcinoma, HMMR expression is significantly higher in tumor tissues than in non-tumor tissues, and high expression of HMMR is associated with gender, pathological stage, lymph node metastasis, and distant metastasis, as well as poor prognosis. GSEA identified 15 signaling pathways enriched in samples with the high HMMR expression phenotype, and the PPI network revealed 10 genes co-expressed with HMMR, indicating its potential as an oncogene and therapeutic target for LUAD.
Hyaluronic acid-mediated motility receptor (HMMR), a tumor-related gene, plays a vital role in the occurrence and progression of various cancers. This research is aimed to reveal the effect of HMMR in lung adenocarcinoma (LUAD). We first obtained the gene expression profiles and clinical data of patients with LUAD from The Cancer Genome Atlas (TCGA) database. Then, based on the TCGA cohort, the HMMR expression difference between LUAD tissues and nontumor tissues was detected and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort, and Gene Expression Omnibus (GEO) cohort. Logistic regression analysis and chi-square test were adopted to study the correlation between HMMR expression and clinicopathological parameters. The effect of HMMR expression on survival was evaluated by Kaplan-Meier survival analysis and using the Cox regression model. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to screen out signaling pathways related to LUAD and the co-expression analysis was employed to build the protein-protein interaction (PPI) network. The HMMR expression level in LUAD tissues was dramatically higher than that in nontumor tissues. Logistic regression analysis and chi-square test demonstrated that the high HMMR expression in LUAD has relation with gender, pathological stage, T classification, lymph node metastasis, and distant metastasis. The Kaplan-Meier curve suggested a poor prognosis for LUAD patients with high HMMR expression. Multivariate analysis implied that the high HMMR expression was a vital independent predictor of poor overall survival (OS). GSEA indicated that a total of 15 signaling pathways were enriched in samples with the high HMMR expression phenotype. The PPI network gave 10 genes co-expressed with HMMR. HMMR may be an oncogene in LUAD and is expected to become a potential prognostic indicator and therapeutic target for LUAD.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Cell Biology

In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion

Na-Na Guan, Chun-Chun Wang, Li Zhang, Li Huang, Jian-Qiang Li, Xue Piao

JOURNAL OF CELLULAR AND MOLECULAR MEDICINE (2020)

Review Biochemistry & Molecular Biology

Computational Models for Self-Interacting Proteins Prediction

Jia Qu, Yan Zhao, Li Zhang, Shu-Bin Cai, Zhong Ming, Chun-Chun Wang

PROTEIN AND PEPTIDE LETTERS (2020)

Article Genetics & Heredity

Differential Expression and Bioinformatics Analysis of circRNA in Non-small Cell Lung Cancer

Qiuwen Sun, Xia Li, Muchen Xu, Li Zhang, Haiwei Zuo, Yong Xin, Longzhen Zhang, Ping Gong

FRONTIERS IN GENETICS (2020)

Review Biochemical Research Methods

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

Li Huang, Li Zhang, Xing Chen

Summary: MicroRNAs (miRNAs) are important gene regulators in the pathogenesis of complex diseases and have potential applications in diagnosis and therapy. Accurate discovery of miRNA-disease associations (MDAs) is crucial for effective miRNA therapy. This review revisits miRNA biogenesis, detection techniques, and functions, summarizes recent experimental findings related to common miRNA-associated diseases, introduces updates of relevant databases and web servers, and discusses the contribution of diverse data sources to accurate MDA prediction.

BRIEFINGS IN BIOINFORMATICS (2022)

Review Biochemical Research Methods

Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models

Li Huang, Li Zhang, Xing Chen

Summary: There is currently no widely accepted strategy for evaluating computational models for microRNA-disease associations (MDAs). The evaluation methods and procedures are often determined on a case-by-case basis and depend on the choices of researchers. This review provides a comprehensive analysis of the evaluation methods used for 29 state-of-the-art models predicting MDAs and recommends a feasible evaluation workflow for future models.

BRIEFINGS IN BIOINFORMATICS (2022)

Review Biochemical Research Methods

Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models

Li Huang, Li Zhang, Xing Chen

Summary: In this review, 29 state-of-the-art models for microRNA-disease association (MDA) prediction based on model fusion and non-fusion are presented. The new taxonomy demonstrates changes in the algorithmic architecture of models compared to earlier classifications. Furthermore, the progress made in overcoming obstacles to effective MDA prediction since 2017 is discussed, and future research directions are proposed for enhancing model performance.

BRIEFINGS IN BIOINFORMATICS (2022)

No Data Available