Integrative analyses identified ion channel genes GJB2 and SCNN1B as prognostic biomarkers and therapeutic targets for lung adenocarcinoma

Ao, Lu; Shi, Yidan; Liu, Yijuan; Lin, Jiahao; Zhang, Huarong; Guo, Yating; Liu, Lisheng; Lin, Zeman; Wu, Junling; Ji, Daihan; Wang, Chengdang

doi:10.1016/j.lungcan.2021.06.001

Cited by 18 publications

(14 citation statements)

References 57 publications

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“…The expression levels of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 could influence the cancer progression [21][22][23][24][25][26]. For instance, ERO1A, also known as ERO1L, promotes IL6R secretion by targeting disulfide bond formation.…”

Section: Discussionmentioning

confidence: 99%

The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

Guo

Liu

et al. 2022

Oxidative Medicine and Cellular Longevity

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Background. Oxidative stress plays a role in carcinogenesis. This study explores the roles of oxidative stress-related genes (OSRGs) in lung adenocarcinoma (LAC). Besides, we construct a risk score model of OSRGs that evaluates the prognosis of LAC patients. Methods. OSRGs were downloaded from the Gene Set Enrichment Analysis (GSEA) website. The expression levels of OSRGs were confirmed in LAC tissues of the TCGA database. GO and KEGG analyses were used to evaluate the roles and mechanisms of oxidative stress-related differentially expressed genes (DEGs). Survival, ROC, Cox analysis, and AIC method were used to screen the prognostic DEGs in LAC patients. Subsequently, we constructed a risk score model of OSRGs and a nomogram. Further, this work investigated the values of the risk score model in LAC progression and the relationship between the risk score model and immune infiltration. Results. We discovered 163 oxidative stress-related DEGs in LAC, involving cellular response to oxidative stress and reactive oxygen species. Besides, the areas under the curve of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 were 0.970, 0.984, 0.984, 0.945, 0.984, 0.771, and 0.959, respectively. This indicates that these OSRGs have diagnosis values of LAC and are significantly related to the overall survival of LAC patients. ERO1A, CDC25C, and ITGB4 overexpressions were independent risk factors for the poor prognosis of LAC patients and were associated with risk scores in the risk model. High-risk score levels affected the poor prognosis of LAC patients. Notably, a high-risk score may be implicated in LAC progression via cell cycle, DNA replication, mismatch repair, and other mechanisms. Further, ERO1A, CDC25C, and ITGB4 expression levels were related to the immune infiltrating cells of LAC, including mast cells, NK cells, and CD8 T cells. Conclusion. In summary, ERO1A, CDC25C, and ITGB4 of OSRGs are associated with poor prognosis of LAC patients. We confirmed that the risk model based on the ERO1A, CDC25C, and ITGB4 is expected to assess the prognosis of LAC patients.

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Section: Discussionmentioning

confidence: 99%

The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

Guo

Liu

et al. 2022

Oxidative Medicine and Cellular Longevity

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“…The study also identified 22 unique ion channels in the metastatic state, of which GRIK2, GJB3, KCNB2, KCNA1 and KCNK2 were previously reported in breast cancer metas-tasis [67][68][69][70][71]. Similarly, GABRG1, KCNQ3, GABRB2, GRIN2A, GRIA4, P2RX6, CACNA2D1, SCNN1G and TRPC4 were previously reported in metastasis in various other tumors [72][73][74][75][76][77][78][79][80][81][82][83]. TRPV3, GABRG3, KCNT1, GJA5, KCNN1, GRIK5, CLCNKB and CHRNB2 were identified as putative ion channels that might play a crucial role in breast cancer metastasis.…”

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confidence: 75%

In Silico Analysis of Ion Channels and Their Correlation with Epithelial to Mesenchymal Transition in Breast Cancer

Parthasarathi

Mandal

Singh

et al. 2022

Cancers

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Uncontrolled growth of breast cells due to altered gene expression is a key feature of breast cancer. Alterations in the expression of ion channels lead to variations in cellular activities, thus contributing to attributes of cancer hallmarks. Changes in the expression levels of ion channels were observed as a consequence of EMT. Additionally, ion channels were reported in the activation of EMT and maintenance of a mesenchymal phenotype. Here, to identify altered ion channels in breast cancer patients, differential gene expression and weighted gene co-expression network analyses were performed using transcriptomic data. Protein–protein interactions network analysis was carried out to determine the ion channels interacting with hub EMT-related genes in breast cancer. Thirty-two ion channels were found interacting with twenty-six hub EMT-related genes. The identified ion channels were further correlated with EMT scores, indicating mesenchymal phenotype. Further, the pathway map was generated to represent a snapshot of deregulated cellular processes by altered ion channels and EMT-related genes. Kaplan–Meier five-year survival analysis and Cox regressions indicated the expression of CACNA1B, ANO6, TRPV3, VDAC1 and VDAC2 to be potentially associated with poor survival. Deregulated ion channels correlate with EMT-related genes and have a crucial role in breast cancer-associated tumorigenesis. Most likely, they are potential candidates for the determination of prognosis in patients with breast cancer.

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“…It has been shown that GJB2 is an independent prognostic biomarker for LUAD, and patients with GJB2 overexpression have shorter overall survival. GJB2 may also be a potential prognostic factor for KIRC, as shown by pan-cancer analysis ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

The global change of gene expression pattern caused by PTEN mutation affects the prognosis of glioblastoma

et al. 2022

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Glioblastoma (GBM), an aggressive primary tumor, is common in humans, accounting for 12–15% of all intracranial tumors, and has median survival of fewer than 15 months. Since a growing body of evidence suggests that conventional drugs are ineffective against GBM, our goal is to find emerging therapies that play a role in its treatment. This research constructs a risk model to predict the prognosis of GBM patients. A set of genes associated with GBM was taken from a GBM gene data bank, and clinical information on patients with GBM was retrieved from the Cancer Genome Atlas (TCGA) data bank. One-way Cox and Kaplan–Meier analyses were performed to identify genes in relation to prognosis. Groups were classified into high and low expression level of PTEN expression. Prognosis-related genes were further identified, and multi-factor Cox regression analysis was used to build risk score equations for the prognostic model to construct a survival prognostic model. The area under the ROC curve suggested that the pattern had high accuracy. When combined with nomogram analysis, GJB2 was considered an independent predictor of GBM prognosis. This study provides a potential prognostic predictive biological marker for GBM patients and confirms that GJB2 is a key gene for GBM progression.

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Integrative analyses identified ion channel genes GJB2 and SCNN1B as prognostic biomarkers and therapeutic targets for lung adenocarcinoma

Abstract: Integrative analyses identified ion channel genes GJB2 and SCNN1B as prognostic biomarkers and therapeutic targets for lung adenocarcinoma

Cited by 18 publications

References 57 publications

The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

In Silico Analysis of Ion Channels and Their Correlation with Epithelial to Mesenchymal Transition in Breast Cancer

The global change of gene expression pattern caused by PTEN mutation affects the prognosis of glioblastoma

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