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Wang R, Lang W, Xue Q, Zhang L, Xujia Y, Wang C, Fang X, Gao S, Guo L. Screening for ferroptosis genes related to endometrial carcinoma and predicting of targeted drugs based on bioinformatics. Arch Toxicol 2024; 98:3155-3165. [PMID: 38758406 DOI: 10.1007/s00204-024-03783-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
Endometrial carcinoma is one of most common malignant tumors in women, and ferroptosis is closely related to the development and treatment of endometrial carcinoma. The aim of this study was to screen ferroptosis-related genes associated with endometrial carcinoma and predict targeted drugs through bioinformatics. 761 differentially expressed genes were obtained by the dataset GSE63678 from the GEO database, and most of the genes were enriched in the KEGG_CELL_CYCLE and KEGG_OOCYTE_MEIOSIS signaling pathways. 22 ferroptosis-differentially expressed genes were obtained by intersection with the FerrDb database. These genes were involved in biological processes including macromolecular complex assembly and others, and involved in signal pathways including glutathione metabolism, p53 signaling pathway and others. CDKN2A, IDH1, NRAS, TFRC and GOT1 were obtained as hub genes by PPI network analysis. GEPIA showed that CDKN2A, IDH1, NRAS and TFRC were significantly expressed in endometrial carcinoma. Immunohistochemical results showed that CDKN2A, NRAS and TFRC were significantly expressed in endometrial carcinoma clinical tissue samples. The ROC constructed by TCGA database showed that CDKN2A, NRAS and TFRC had significant value in the diagnosis of endometrial carcinoma, and all had prognostic efficacy. 136,572-09-3 BOSS and others were identified as potential targeted drugs for endometrial carcinoma targeting ferroptosis. Our study has shown that ferroptosis-related genes CDKN2A, NRAS and TFRC are diagnostic markers of endometrial carcinoma, and 136,572-09-3 BOSS, methyprylon BOSS, daunorubicin CTD 00005752, nitroglycerin BOSS and dUTP BOSS, IRON BOSS, Imatinib mesylate BOSS, 2-Butanone BOSS, water BOSS, and L-thyroxine BOSS may be potential therapeutic drugs.
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Affiliation(s)
- Rui Wang
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Wei Lang
- Department of Gynecology III, Changchun Obstetrics-Gynecology Hospital, Changchun, 130042, Jilin Province, China
| | - Qian Xue
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Le Zhang
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Yunzhu Xujia
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Chaofan Wang
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Xin Fang
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Shidi Gao
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China
| | - Li Guo
- Department of Toxicology, School of Public Health, Jilin University, Changchun, 130021, Jilin Province, China.
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Cheng S, Wang X, Yang S, Liang J, Song C, Zhu Q, Chen W, Ren Z, Zhu F. Identification of novel disulfidptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of skin cutaneous melanoma patients. Skin Res Technol 2024; 30:e13814. [PMID: 38924611 PMCID: PMC11197043 DOI: 10.1111/srt.13814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/04/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND Skin cutaneous melanoma (SKCM) is an aggressive form of malignant melanoma with poor prognosis and high mortality rates. Disulfidptosis is a newly discovered cell death regulatory mechanism caused by the abnormal accumulation of disulfides. This unique pathway is guiding significant new research to understand cancer progression for targeted treatment. However, the correlation between disulfidptosis with long non-coding RNAs (lncRNAs) in SKCM remains unknown at present. METHODS The Cancer Genome Atlas database furnished lncRNA expression data and clinical information for SKCM patients. Pearson correlation and Cox regression analyses identified disulfidptosis-related lncRNAs associated with SKCM prognosis. ROC curves and a nomogram validated the model. TME, immune infiltration, GSEA analysis, immune checkpoint gene expression profiling, and drug sensitivity were assessed in high and low-risk groups. Consistent clustering categorized SKCM patients for personalized clinical treatment guidance. RESULTS A total of twelve disulfidptosis-related lncRNAs were identified for the development of prognosis prediction models. The area under the curve (AUC) values of the ROC curve and the nomogram provided reliable discrimination to evaluate the prognostic potential for SKCM patients. The TME played a crucial role in tumorigenesis, progression and prognosis, and the risk scores were closely related to immune cell infiltration. Meanwhile, the combination of chemotherapy, targeted therapy, and immunotherapy was recommended for low-risk patients based on drug sensitivity and immune efficacy analyses. CONCLUSION We identified a risk model of twelve disulfidptosis-related lncRNAs that could be used to predict the prognosis of SKCM patients and help guide immunotherapy and chemotherapy for personalized treatment plans.
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Affiliation(s)
- Shengrong Cheng
- Department of Plastic SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Xin Wang
- Faculty of Medicine and Health SciencesGhent UniversityGhentBelgium
| | - Shuhan Yang
- Department of General SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Jiahui Liang
- Department of General SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Department of Breast SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Caiying Song
- Department of Plastic SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Qiuxuan Zhu
- Department of Plastic SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Wendong Chen
- Department of Plastic SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Zhiyao Ren
- Faculty of Medicine and Health SciencesGhent UniversityGhentBelgium
| | - Fei Zhu
- Department of Plastic SurgeryThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
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Zhang R, Zhao W, Zhu X, Liu Y, Ding Q, Yang C, Zou H. Identification of hub genes distinguishing subtypes in endometrial stromal sarcoma through comprehensive bioinformatics analysis. Sci Rep 2024; 14:37. [PMID: 38167455 PMCID: PMC10761685 DOI: 10.1038/s41598-023-47668-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 11/16/2023] [Indexed: 01/05/2024] Open
Abstract
Diagnosing low-grade and high-grade endometrial stromal sarcoma (LG-ESS and HG-ESS) is a challenge. This study aimed to identify biomarkers. 22 ESS cases were analyzed using Illumina microarrays. Differentially expressed genes (DEGs) were identified via Limma. DEGs were analyzed with String and Cytoscape. Core genes were enriched with GO and KEGG, their pan-cancer implications and immune aspects were studied. 413 DEGs were found by exome sequencing, 2174 by GSE85383 microarray. 36 common genes were identified by Venn analysis, and 10 core genes including RBFOX1, PCDH7, FAT1 were selected. Core gene GO enrichment included cell adhesion, T cell proliferation, and KEGG focused on related pathways. Expression was evaluated across 34 cancers, identifying immune DEGs IGF1 and AVPR1A. Identifying the DEGs not only helps improve our understanding of LG-ESS, HG-ESS but also promises to be potential biomarkers for differential diagnosis between LG-ESS and HG-ESS and new therapeutic targets.
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Affiliation(s)
- Ruiqi Zhang
- Department of Pathology, The First Affiliated Hospital, Shihezi University School of Medicine, Xinjiang, 832002, China
| | - Weilin Zhao
- Department of Pathology, Taihe Hospital, Hubei University of Medicine, Hubei, 442000, China
| | - Xingyao Zhu
- Department of Pathology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, 310009, China
| | - Yuhua Liu
- Department of Pathology, The First Affiliated Hospital, Shihezi University School of Medicine, Xinjiang, 832002, China
| | - Qi Ding
- Department of Pathology, The First Affiliated Hospital, Shihezi University School of Medicine, Xinjiang, 832002, China
| | - Caiyun Yang
- Department of Pathology, The First Affiliated Hospital, Shihezi University School of Medicine, Xinjiang, 832002, China
| | - Hong Zou
- Department of Pathology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, 310009, China.
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Kim N, Kim MH, Pyo J, Lee SM, Jang JS, Lee DW, Kim KW. CCR8 as a Therapeutic Novel Target: Omics-Integrated Comprehensive Analysis for Systematically Prioritizing Indications. Biomedicines 2023; 11:2910. [PMID: 38001911 PMCID: PMC10669377 DOI: 10.3390/biomedicines11112910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/10/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
Target identification is a crucial process in drug development, aiming to identify key proteins, genes, and signal pathways involved in disease progression and their relevance in potential therapeutic interventions. While C-C chemokine receptor 8 (CCR8) has been investigated as a candidate anti-cancer target, comprehensive multi-omics analyzes across various indications are limited. In this study, we conducted an extensive bioinformatics analysis integrating genomics, proteomics, and transcriptomics data to establish CCR8 as a promising anti-cancer drug target. Our approach encompassed data collection from diverse knowledge resources, gene function analysis, differential gene expression profiling, immune cell infiltration assessment, and strategic prioritization of target indications. Our findings revealed strong correlations between CCR8 and specific cancers, notably Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), and Thyroid carcinoma (THCA). This research advances our understanding of CCR8 as a potential target for anti-cancer drug development, bridging the gap between molecular insights and creating opportunities for personalized treatment of solid tumors.
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Affiliation(s)
- Nari Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
| | - Mi-Hyun Kim
- Research Institute, Trial Informatics Inc., Seoul 05544, Republic of Korea;
| | - Junhee Pyo
- College of Pharmacy, Chungbuk National University, Cheongju 28644, Republic of Korea;
| | - Soo-Min Lee
- Samjin Pharmaceutical Co., Ltd., Seoul 04054, Republic of Korea;
| | - Ji-Sung Jang
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Republic of Korea;
| | - Do-Wan Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
| | - Kyung Won Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
- Research Institute, Trial Informatics Inc., Seoul 05544, Republic of Korea;
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
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Shao H, Zhu J, Zhu Y, Liu L, Zhao S, Kang Q, Liu Y, Zou H. Identification of characteristic genes and construction of regulatory network in gallbladder carcinoma. BMC Med Genomics 2023; 16:240. [PMID: 37821907 PMCID: PMC10566037 DOI: 10.1186/s12920-023-01663-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/17/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Gallbladder carcinoma (GBC) is a highly malignant tumor with a poor overall prognosis. This study aimed to identify the characteristic microRNAs (miRNAs) of GBC and the competing endogenous RNA (ceRNA) regulatory mechanisms. METHODS The microarray data of GBC tissue samples and normal gallbladder (NGB) tissue samples from the Gene Expression Omnibus (GEO) database was downloaded. GBC-related differentially expressed miRNAs (DE-miRNAs) were identified by inter-group differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were used to screen the characteristic miRNA based on the intersect between least absolute shrinkage and selection operator (LASSO) and Support vector machine-recursive feature elimination (SVM-RFE). Based on the differential expression analysis of GEO database, the ceRNA network of characteristic miRNA was predicted and constructed. The biological functions of the ceRNA network were revealed by carrying out the gene enrichment analysis was implemented. We further screened the key genes of ceRNA network and constructed a protein-protein interaction (PPI) network, and predicted and generated the transcription factors (TFs) network of signature miRNAs. The expression of characteristic miRNA in clinical samples was verified by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS A total of 131 GBC-related DE-miRNAs were obtained. The hsa-miR-4770 was defined as characteristic miRNA for GBC. The ceRNA network containing 211 mRNAs, one miRNA, two lncRNAs, and 48 circRNAs was created. Gene enrichment analysis suggested that the downstream genes were mainly involved in actin filament organization, cell-substrate adhesion, cell-matrix adhesion, reactive oxygen species metabolic process, glutamine metabolic process and extracellular matrix (ECM)-receptor interaction pathway. 10 key genes in the network were found to be most correlated with disease, and involved in cell cycle-related processes, p53, and extrinsic apoptotic signaling pathways. The qRT-PCR result demonstrated that hsa-miR-4770 is down-regulated in GBC, and the expression trend is consistent with the public database. CONCLUSIONS We identified hsa-miR-4770 as the characteristic miRNA for GBC. The ceRNA network of hsa-miR-4770 may play key roles in GBC. This study provided some basis for potential pathogenesis of GBC.
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Affiliation(s)
- Hanrui Shao
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China
| | - Jiahai Zhu
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China
| | - Ya Zhu
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China
| | - Lixin Liu
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China
| | - Songling Zhao
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China
| | - Qiang Kang
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China
| | - Yunxia Liu
- Experiment Teaching Center, Basic Medical School, Kunming Medical University, 1168 West Chunrong Road, Kunming, 650500, P.R. China.
| | - Hao Zou
- Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Avenue, Wu Hua District, Kunming, 650106, Yunnan, P.R. China.
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Khojasteh-Leylakoohi F, Mohit R, Khalili-Tanha N, Asadnia A, Naderi H, Pourali G, Yousefli Z, Khalili-Tanha G, Khazaei M, Maftooh M, Nassiri M, Hassanian SM, Ghayour-Mobarhan M, Ferns GA, Shahidsales S, Lam AKY, Giovannetti E, Nazari E, Batra J, Avan A. Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer. Sci Rep 2023; 13:16678. [PMID: 37794108 PMCID: PMC10551021 DOI: 10.1038/s41598-023-42928-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/16/2023] [Indexed: 10/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.
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Affiliation(s)
- Fatemeh Khojasteh-Leylakoohi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reza Mohit
- Department of Anesthesia, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Nima Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Asadnia
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Naderi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Yousefli
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mina Maftooh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Nassiri
- Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Falmer, Brighton, BN1 9PH, Sussex, UK
| | | | - Alfred King-Yin Lam
- Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast Campus, Gold Coast, QLD, 4222, Australia
| | - Elisa Giovannetti
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam U.M.C., VU. University Medical Center (VUMC), Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start up Unit, Fondazione Pisana Per La Scienza, Pisa, Italy
| | - Elham Nazari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Health Information, Technology and Management, School of Allied Medical Sciences, Shahid BeheshtiUniversity of Medical Science, Tehran, Iran.
| | - Jyotsna Batra
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4000, Australia
- Translational Research Institute, Queensland University of Technology, Brisbane, 4102, Australia
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4000, Australia.
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Xiao W, Xu Y, Baak JP, Dai J, Jing L, Zhu H, Gan Y, Zheng S. Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer. BMC Complement Med Ther 2023; 23:345. [PMID: 37770919 PMCID: PMC10537544 DOI: 10.1186/s12906-023-04148-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/31/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Most lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated. METHODS Ultra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database. RESULTS Seventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients. CONCLUSIONS This study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.
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Affiliation(s)
- Wenke Xiao
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yaxin Xu
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Jan P Baak
- Stavanger University Hospital, Stavanger, 4068, Norway
- Dr. Med Jan Baak AS, Tananger, 4056, Norway
| | - Jinrong Dai
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Lijia Jing
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Hongxia Zhu
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yanxiong Gan
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| | - Shichao Zheng
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
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Mottaghi-Dastjerdi N, Ghorbani A, Montazeri H, Guzzi PH. A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis. BMC Gastroenterol 2023; 23:248. [PMID: 37482618 PMCID: PMC10364406 DOI: 10.1186/s12876-023-02891-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/18/2023] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis. METHODS Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes. RESULTS Eleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages. CONCLUSIONS These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.
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Affiliation(s)
- Negar Mottaghi-Dastjerdi
- Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran.
| | - Abozar Ghorbani
- Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, Iran.
| | - Hamed Montazeri
- Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
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Mosharaf MP, Reza MS, Gov E, Mahumud RA, Mollah MNH. Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis. Vaccines (Basel) 2022; 10:vaccines10050771. [PMID: 35632527 PMCID: PMC9143695 DOI: 10.3390/vaccines10050771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 12/10/2022] Open
Abstract
Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.
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Affiliation(s)
- Md. Parvez Mosharaf
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- School of Commerce, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Md. Selim Reza
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- Centre for High Performance Computing, Joint Engineering Research Centre for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana AlparslanTurkes Science and Technology University, Adana 01250, Turkey;
| | - Rashidul Alam Mahumud
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia;
| | - Md. Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.P.M.); (M.S.R.)
- Correspondence:
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