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Xu Z, Zhou H, Luo Y, Li N, Chen S. Bioinformatics analysis and validation of CSRNP1 as a key prognostic gene in non-small cell lung cancer. Heliyon 2024; 10:e28412. [PMID: 38560128 PMCID: PMC10979096 DOI: 10.1016/j.heliyon.2024.e28412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
Cysteine and serine-rich nuclear protein 1 (CSRNP1) has shown prognostic significance in various cancers, but its role in non-small cell lung cancer (NSCLC) remains elusive. We investigated CSRNP1 expression in NSCLC cases using bioinformatics tools from the GEO public repository and validated our findings through RT-qPCR in tumor and adjacent normal tissues. KEGG and GO enrichment analyses were employed to unveil the significant deregulation in signaling pathways. Additionally, clinical significance of CSRNP1 in NSCLC was determined through receiver operating curve (ROC) analysis, and its impact on survival was assessed using Kaplan-Meier analysis. To explore the functional impact of CSRNP1, we silenced its expression in NSCLC cells and assessed the effects on cell viability, migration, and invasion using MTT, Transwell, and wound-healing assays, respectively. Additionally, we investigated the influence of CSRNP1 silencing on the phosphorylation patterns of critical signaling proteins such as p53, p-Akt, and p-MDM2. Our results demonstrated significantly lower CSRNP1 expression in NSCLC tumor tissues (P < 0.01). ROC analysis indicated that NSCLC patients with high CSRNP1 expression exhibited extended overall survival and disease-free survival. Furthermore, CSRNP1 silencing promoted NSCLC cells viability, migration, and invasion (P < 0.05). Mechanistically, CSRNP1 silencing led to increased phosphorylation of AKT and MDM2, along with a concurrent reduction in p53 protein expression, suggesting its impact on NSCLC through deregulated cell cycle processes. In conclusion, our study underscores the significance of CSRNP1 in NSCLC pathogenesis, offering insights for targeted therapeutic interventions of NSCLC.
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Affiliation(s)
- Zhongneng Xu
- Department of Thoracic Surgery, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Hao Zhou
- Department of Thoracic Surgery, Guanyun People's Hospital, Guanyun, Sichuan, 222299, China
| | - Yonggang Luo
- Department of Thoracic Surgery, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
| | - Nunu Li
- Department of Sanatorium 1, Air Force Health Care Center for Special Service Hangzhou Sanatorium 5, Hangzhou, Zhejiang, 310002, China
| | - Sheng Chen
- Department of Thoracic Surgery, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China
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White B, Swietach P. What can we learn about acid-base transporters in cancer from studying somatic mutations in their genes? Pflugers Arch 2024; 476:673-688. [PMID: 37999800 PMCID: PMC11006749 DOI: 10.1007/s00424-023-02876-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/24/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
Acidosis is a chemical signature of the tumour microenvironment that challenges intracellular pH homeostasis. The orchestrated activity of acid-base transporters of the solute-linked carrier (SLC) family is critical for removing the end-products of fermentative metabolism (lactate/H+) and maintaining a favourably alkaline cytoplasm. Given the critical role of pH homeostasis in enabling cellular activities, mutations in relevant SLC genes may impact the oncogenic process, emerging as negatively or positively selected, or as driver or passenger mutations. To address this, we performed a pan-cancer analysis of The Cancer Genome Atlas simple nucleotide variation data for acid/base-transporting SLCs (ABT-SLCs). Somatic mutation patterns of monocarboxylate transporters (MCTs) were consistent with their proposed essentiality in facilitating lactate/H+ efflux. Among all cancers, tumours of uterine corpus endometrial cancer carried more ABT-SLC somatic mutations than expected from median tumour mutation burden. Among these, somatic mutations in SLC4A3 had features consistent with meaningful consequences on cellular fitness. Definitive evidence for ABT-SLCs as 'cancer essential' or 'driver genes' will have to consider microenvironmental context in genomic sequencing because bulk approaches are insensitive to pH heterogeneity within tumours. Moreover, genomic analyses must be validated with phenotypic outcomes (i.e. SLC-carried flux) to appreciate the opportunities for targeting acid-base transport in cancers.
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Affiliation(s)
- Bobby White
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford, OX1 3PT, UK.
| | - Pawel Swietach
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford, OX1 3PT, UK
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Sun KF, Sun LM, Zhou D, Chen YY, Hao XW, Liu HR, Liu X, Chen JJ. XGBG: A Novel Method for Identifying Ovarian Carcinoma Susceptible Genes Based on Deep Learning. Front Oncol 2022; 12:897503. [PMID: 35646648 PMCID: PMC9133413 DOI: 10.3389/fonc.2022.897503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/08/2022] [Indexed: 11/30/2022] Open
Abstract
Ovarian carcinomas (OCs) represent a heterogeneous group of neoplasms consisting of several entities with pathogenesis, molecular profiles, multiple risk factors, and outcomes. OC has been regarded as the most lethal cancer among women all around the world. There are at least five main types of OCs classified by the fifth edition of the World Health Organization of tumors: high-/low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, and endometrioid carcinoma. With the improved knowledge of genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) analyses, the knowledge of genomic landscape of complex diseases has been uncovered in large measure. Moreover, pathway analyses also play an important role in exploring the underlying mechanism of complex diseases by providing curated pathway models and information about molecular dynamics and cellular processes. To investigate OCs deeper, we introduced a novel disease susceptible gene prediction method, XGBG, which could be used in identifying OC-related genes based on different omics data and deep learning methods. We first employed the graph convolutional network (GCN) to reconstruct the gene features based on both gene feature and network topological structure. Then, a boosting method is utilized to predict OC susceptible genes. As a result, our model achieved a high AUC of 0.7541 and an AUPR of 0.8051, which indicates the effectiveness of the XGPG. Based on the newly predicted OC susceptible genes, we gathered and researched related literatures to provide strong support to the results, which may help in understanding the pathogenesis and mechanisms of the disease.
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Affiliation(s)
- Ke Feng Sun
- Department of Obstetrics and Gynecology, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Li Min Sun
- Department of Oncology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Dong Zhou
- Department of Oncology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ying Ying Chen
- Department of Nephrology, The First Affiliated Hospital of Heilongjiang University of Chinese Medical, Harbin, China
| | - Xi Wen Hao
- Heilongjiang University of Chinese Medicine, Harbin, China
| | - Hong Ruo Liu
- Department of Oncology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xin Liu
- Department of Oncology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jing Jing Chen
- Department of Rheumatology and Immunology, The First Hospital Affiliated to Army Medical University, Chongqing, China
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Ye L, Zhang Y, Yang X, Shen F, Xu B. An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods. Front Cell Dev Biol 2021; 9:730475. [PMID: 34485310 PMCID: PMC8414800 DOI: 10.3389/fcell.2021.730475] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 07/22/2021] [Indexed: 12/12/2022] Open
Abstract
Ovarian cancer (OC) is one of the most fatal diseases among women all around the world. It is highly lethal because it is usually diagnosed at an advanced stage which may reduce the survival rate greatly. Even though most of the patients are treated timely and effectively, the survival rate is still low due to the high recurrence rate of OC. With a large number of genome-wide association analysis (GWAS)-discovered risk regions of OC, expression quantitative trait locus (eQTL) analyses can explore candidate susceptible genes based on these risk loci. However, a large number of OC-related genes remain unknown. In this study, we proposed a novel gene prediction method based on different omics data and deep learning methods to identify OC causal genes. We first employed graph attention network (GAT) to obtain a compact gene feature representation, then a deep neural network (DNN) is utilized to predict OC-related genes. As a result, our model achieved a high AUC of 0.761 and AUPR of 0.788, which proved the accuracy and effectiveness of our proposed method. At last, we conducted a gene-set enrichment analysis to further explore the mechanism of OC. Finally, we predicted 245 novel OC causal genes and 10 top related KEGG pathways.
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Affiliation(s)
- Lu Ye
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yi Zhang
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Xinying Yang
- Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Fei Shen
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Bo Xu
- Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Patel D, Zhang X, Farrell JJ, Lunetta KL, Farrer LA. Set-Based Rare Variant Expression Quantitative Trait Loci in Blood and Brain from Alzheimer Disease Study Participants. Genes (Basel) 2021; 12:419. [PMID: 33804025 PMCID: PMC7999141 DOI: 10.3390/genes12030419] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/04/2021] [Accepted: 03/10/2021] [Indexed: 12/12/2022] Open
Abstract
Because studies of rare variant effects on gene expression have limited power, we investigated set-based methods to identify rare expression quantitative trait loci (eQTL) related to Alzheimer disease (AD). Gene-level and pathway-level cis rare-eQTL mapping was performed genome-wide using gene expression data derived from blood donated by 713 Alzheimer's Disease Neuroimaging Initiative participants and from brain tissues donated by 475 Religious Orders Study/Memory and Aging Project participants. The association of gene or pathway expression with a set of all cis potentially regulatory low-frequency and rare variants within 1 Mb of genes was evaluated using SKAT-O. A total of 65 genes expressed in the brain were significant targets for rare expression single nucleotide polymorphisms (eSNPs) among which 17% (11/65) included established AD genes HLA-DRB1 and HLA-DRB5. In the blood, 307 genes were significant targets for rare eSNPs. In the blood and the brain, GNMT, LDHC, RBPMS2, DUS2, and HP were targets for significant eSNPs. Pathway enrichment analysis revealed significant pathways in the brain (n = 9) and blood (n = 16). Pathways for apoptosis signaling, cholecystokinin receptor (CCKR) signaling, and inflammation mediated by chemokine and cytokine signaling were common to both tissues. Significant rare eQTLs in inflammation pathways included five genes in the blood (ALOX5AP, CXCR2, FPR2, GRB2, IFNAR1) that were previously linked to AD. This study identified several significant gene- and pathway-level rare eQTLs, which further confirmed the importance of the immune system and inflammation in AD and highlighted the advantages of using a set-based eQTL approach for evaluating the effect of low-frequency and rare variants on gene expression.
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Affiliation(s)
- Devanshi Patel
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA;
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - John J. Farrell
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA;
| | - Lindsay A. Farrer
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA;
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA;
- Department of Ophthalmology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
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Wang Z, Wang Z, Niu X, Liu J, Wang Z, Chen L, Qin B. Identification of seven-gene signature for prediction of lung squamous cell carcinoma. Onco Targets Ther 2019; 12:5979-5988. [PMID: 31440059 PMCID: PMC6664418 DOI: 10.2147/ott.s198998] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/13/2019] [Indexed: 12/24/2022] Open
Abstract
Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.
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Affiliation(s)
- Zhe Wang
- Department of Gastrointestinal Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, People's Republic of China
| | - Zhongmiao Wang
- Department of Gastrointestinal Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, People's Republic of China
| | - Xing Niu
- Department of Second Clinical College, Shengjing Hospital affiliated to China Medical University, Shenyang 110004, Liaoning Province, People's Republic of China
| | - Jie Liu
- Science Experiment Center of China Medical University, China Medical University, Shenyang 110122, Liaoning Province, People's Republic of China
| | - Zhuning Wang
- Department of Second Clinical College, Shengjing Hospital affiliated to China Medical University, Shenyang 110004, Liaoning Province, People's Republic of China
| | - Lijie Chen
- Department of Third Clinical College, China Medical University, Shenyang 110122, Liaoning Province, People's Republic of China
| | - Baoli Qin
- Department of Gastrointestinal Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, People's Republic of China
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