Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis

Jiang, Jie; Liu, Chong; Xu, Guoyong; Liang, Tuo; Yu, Chaojie; Liao, Shijie; Zhang, Zide; Lu, Zhaojun; Wang, Zequn; Chen, Jiarui; Tianyou, Chen; Li, Hao; Zhan, Xinli

doi:10.3389/fonc.2021.621430

Cited by 13 publications

(14 citation statements)

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“…With the continuous advancement of bioinformatics, more and more bioinformatics techniques are being used to guide clinical practice and application (8)(9)(10)(11). The high prevalence, expensive treatment, and high mortality rate of gastric cancer warrants urgent identification of prognostic biomarkers for gastric cancer for guiding its clinical diagnosis and prognosis.…”

Section: Introductionmentioning

confidence: 99%

Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

et al. 2021

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IntroductionGastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.Materials and MethodsGene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.ResultsAfter five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.ConclusionCGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.

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Section: Introductionmentioning

confidence: 99%

Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

et al. 2021

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“…WGCNA is an advanced bioinformatics analysis that is widely used in biological lineage information to find the biological modules most associated with the disease ( 15 , 16 ). A gene co-expression network is an undirected graph, where each node represents a gene and can be established by observing the gene pairs that produce similar expressions between different samples.…”

Section: Methodsmentioning

confidence: 99%

Dysregulation of SAA1, TUBA8 and Monocytes Are Key Factors in Ankylosing Spondylitis With Femoral Head Necrosis

et al. 2022

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IntroductionThe mechanism of ankylosing spondylitis with femoral head necrosis is unknown, and our study aimed investigate the effects of genetic and immune cell dysregulation on ankylosing spondylitis.Materials and MethodsThe protein expression of all ligaments in ankylosing spondylitis with femoral head necrosis was obtained using label-free quantification protein park analysis of six pairs of specimens. The possible pathogenesis was explored using differential protein analysis, weighted gene co-expression network analysis, recording intersections with hypoxia-related genes, immune cell correlation analysis, and drug sensitivity analysis. Finally, routine blood test data from 502 AS and 162 healthy controls were collected to examine immune cell differential analysis.ResultsSAA1 and TUBA8 were significantly expressed differentially in these two groups and correlated quite strongly with macrophage M0 and resting mast cells (P < 0.05). Routine blood data showed that monocytes were significantly more expressed in AS than in healthy controls (P < 0.05). SAA1 and TUBA8 were closely related to the sensitivity of various drugs, which might lead to altered drug sensitivity.ConclusionDysregulation of SAA1, TUBA8 and monocytes are key factors in ankylosing spondylitis with femoral head necrosis.

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“…The proteins corresponding to differentially expressed MMRGs were used to construct protein-protein interaction (PPI) network with the STRING database. The Cytohubba plug-in in Cytoscape 3.7.2 software was used to analyze the PPI network to obtain hub molecules [ 29 ]. The maximal clique centrality (MCC) algorithm was used to obtain the top 10 genes as the hub genes [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The Cytohubba plug-in in Cytoscape 3.7.2 software was used to analyze the PPI network to obtain hub molecules [ 29 ]. The maximal clique centrality (MCC) algorithm was used to obtain the top 10 genes as the hub genes [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway‐Related Genes in Lung Cancers

Zhang

Kong

et al. 2021

Oxidative Medicine and Cellular Longevity

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Background. Mitochondria are the energy factories of cells. The abnormality of mitochondrial energy metabolism pathways is closely related to the occurrence and development of lung cancer. The abnormal genes in mitochondrial energy metabolism pathways might be the novel targets and biomarkers to diagnose and treat lung cancers. Method. Genes in major mitochondrial energy metabolism pathways were obtained from the KEGG database. The transcriptomic, mutation, and clinical data of lung cancers were obtained from The Cancer Genome Atlas (TCGA) database. Genes and clinical biomarkers were mined that affected lung cancer survival. Gene enrichment analysis was performed with ClusterProfiler and the gene set enrichment analysis (GSEA). STRING database and Cytoscape were used for protein-protein interaction (PPI) analysis. The diagnostic biomarker pattern of lung cancer was optimized, and its accuracy was verified with 10-fold cross-validation. The four genes screened by logistic regression model were verified by western blot in 5 pairs of lung cancer specimens collected in hospital. Results. In total, 188 mitochondrial energy metabolism pathway-related genes (MMRGs) were included in this study. GSEA analysis found that MMRGs in the lung cancer group were mainly enriched in the metabolic pathway of oxidative phosphorylation and electron respiratory transport chain compared to the control group. Age did not affect the mutation frequency of MMRGs. Comparative analysis of these 188 MMRGs identified 43 differentially expressed MMRGs (24 upregulated and 19 downregulated) in the lung cancer group compared to the control group. The survival analysis of these 43 differentially expressed MMRGs found that the survival time was better in the low-expressed GAPDHS group than that in the high-expressed GAPDHS group of lung cancers. The advanced age, high expression of GAPDHS, low expressions of ACSBG1 and CYP4A11, and ACOX3 mutation were biomarkers of poor prognosis in lung cancers. PPI analysis showed that proteins such as GAPDH and GAPDHS interacted with many proteins in mitochondrial metabolic pathways. A four-MMRG-signature model ( y = 0.0069 ∗ ACADL − 0.001 ∗ ALDH 18 A 1 − 0.0405 ∗ CPT 1 B + 0.0008 ∗ PPARG − 1.625 ) was established to diagnose lung cancer with the accuracy up to 98.74%, AUC value up to 0.992, and a missed diagnosis rate of only 0.6%. Western blotting showed that ALDH18A1 and CPT1B proteins were significantly overexpressed in the lung cancer group ( p < 0.05 ), and ACADL and PPARG proteins were slightly underexpressed in the lung cancer group ( p < 0.05 ), which were consistent with the results of their corresponding mRNA expressions. Conclusion. Mitochondrial energy metabolism pathway alterations are the important hallmarks of lung cancer. Age did not increase the risk of MMRG mutation. High expression of GAPDHS, low expression of ACSBG1, low expression of CYP4A11, mutated ACOX3, and old age predict a poor prognosis of lung cancer. Four differentially expressed MMRGs (ACADL, ALDH18A1, CPT1B, and PPARG) established a logistic regression model, which could effectively diagnose lung cancer. At the protein level, ALDH18A1 and CPT1B were significantly upregulated, and ACADL and PPARG were slightly underexpressed, in the lung cancer group compared to the control group, which were consistent with the results of their corresponding mRNA expressions.

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Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis

Cited by 13 publications

References 40 publications

Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

Dysregulation of SAA1, TUBA8 and Monocytes Are Key Factors in Ankylosing Spondylitis With Femoral Head Necrosis

Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway‐Related Genes in Lung Cancers

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