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Chawhan AP, Dsouza N. Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes. Mol Genet Genomics 2024; 299:76. [PMID: 39097557 DOI: 10.1007/s00438-024-02169-8] [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: 03/03/2024] [Accepted: 07/24/2024] [Indexed: 08/05/2024]
Abstract
Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.
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Affiliation(s)
| | - Norine Dsouza
- Department of Biotechnology, St. Xavier's College, Mumbai, Maharashtra, 400001, India.
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2
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Mandelkow T, Bady E, Lurati MCJ, Raedler JB, Müller JH, Huang Z, Vettorazzi E, Lennartz M, Clauditz TS, Lebok P, Steinhilper L, Woelber L, Sauter G, Berkes E, Bühler S, Paluchowski P, Heilenkötter U, Müller V, Schmalfeldt B, von der Assen A, Jacobsen F, Krech T, Krech RH, Simon R, Bernreuther C, Steurer S, Burandt E, Blessin NC. Automated Prognosis Marker Assessment in Breast Cancers Using BLEACH&STAIN Multiplexed Immunohistochemistry. Biomedicines 2023; 11:3175. [PMID: 38137396 PMCID: PMC10741079 DOI: 10.3390/biomedicines11123175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 11/12/2023] [Accepted: 11/18/2023] [Indexed: 12/24/2023] Open
Abstract
Prognostic markers in routine clinical management of breast cancer are often assessed using RNA-based multi-gene panels that depend on fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for an improved risk assessment. To enable automated prognosis marker detection (i.e., progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], GATA3, TROP2, HER2, PD-L1, Ki67, TOP2A), a framework for automated breast cancer identification was developed and validated involving thirteen different artificial intelligence analysis steps and an algorithm for cell distance analysis using 11+1-marker-BLEACH&STAIN-mfIHC staining in 1404 invasive breast cancers of no special type (NST). The framework for automated breast cancer detection discriminated normal glands from malignant glands with an accuracy of 98.4%. This approach identified that five (PR, ER, AR, GATA3, PD-L1) of nine biomarkers were associated with prolonged overall survival (p ≤ 0.0095 each) and two of these (PR, AR) were found to be independent risk factors in multivariate analysis (p ≤ 0.0151 each). The combined assessment of PR-ER-AR-GATA3-PD-L1 as a five-marker prognosis score showed strong prognostic relevance (p < 0.0001) and was an independent risk factor in multivariate analysis (p = 0.0034). Automated breast cancer detection in combination with an artificial intelligence-based analysis of mfIHC enables a rapid and reliable analysis of multiple prognostic parameters. The strict limitation of the analysis to malignant cells excludes the impact of fluctuating tumor purity on assay precision.
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Affiliation(s)
- Tim Mandelkow
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Elena Bady
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Magalie C. J. Lurati
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Jonas B. Raedler
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
- College of Arts and Sciences, Boston University, Boston, MA 02215, USA
| | - Jan H. Müller
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Zhihao Huang
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Eik Vettorazzi
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Maximilian Lennartz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Till S. Clauditz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Patrick Lebok
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
- Institute of Pathology, Clinical Center Osnabrück, 49076 Osnabrück, Germany
| | - Lisa Steinhilper
- Department of Gynecology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Linn Woelber
- Department of Gynecology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Guido Sauter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Enikö Berkes
- Department of Gynecology, Albertinen Clinic Schnelsen, 22457 Hamburg, Germany
| | - Simon Bühler
- Department of Gynecology, Amalie Sieveking Clinic, 22359 Hamburg, Germany
| | - Peter Paluchowski
- Department of Gynecology, Regio Clinic Pinneberg, 25421 Pinneberg, Germany
| | - Uwe Heilenkötter
- Department of Gynecology, Clinical Centre Itzehoe, 25524 Itzehoe, Germany
| | - Volkmar Müller
- Department of Gynecology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Barbara Schmalfeldt
- Department of Gynecology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | | | - Frank Jacobsen
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Till Krech
- Institute of Pathology, Clinical Center Osnabrück, 49076 Osnabrück, Germany
| | - Rainer H. Krech
- Institute of Pathology, Clinical Center Osnabrück, 49076 Osnabrück, Germany
| | - Ronald Simon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Christian Bernreuther
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Stefan Steurer
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Eike Burandt
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Niclas C. Blessin
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
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3
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Meng L, Jiang Y, You J, Zhao P, Liu W, Zhao N, Yu Z, Ma J. IRF4 as a novel target involved in malignant transformation of oral submucous fibrosis into oral squamous cell carcinoma. Sci Rep 2023; 13:2775. [PMID: 36797470 PMCID: PMC9935854 DOI: 10.1038/s41598-023-29936-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) in the context of oral submucous fibrosis (OSF) has a high incidence owing to undefined pathogenesis. Identifying key genes and exploring the underlying molecular mechanisms involved in the conversion of OSF into OSCC are in urgent need. Differentially expressed genes (DEGs) between OSCC and OSF were dug from GEO databases and a total of 170 DEGs were acquired. Functional association of DEGs were analyzed by GO and KEGG. Protein-protein interactions (PPIs) analysis was carried out and candidate biomarkers were identified by Gene co-expression analysis and Cox analyses. Hub genes were confirmed by qRT-PCR in tissues and cell lines, of which we found that IRF4 mRNA was successively up-regulated from Normal to OSF and then to OSCC and associated with immune infiltrating levels. In addition, Immunohistochemical (IHC) and Immunofluorescence (IF) assays were conducted to validate the consistent upregulation of IRF4 and the oncogene role of IRF4 in OSF and OSCC at translation level. IRF4 may be indicative biomarker in transformation of OSF into OSCC. High IRF4 expression contribute to increased immune infiltration of OSCC and may provide a novel diagnostic marker for OSCC patients translated from OSF.
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Affiliation(s)
- Li Meng
- grid.89957.3a0000 0000 9255 8984Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, 140 Hanzhong Road, Nanjing, 210029 China
| | - Yucheng Jiang
- grid.89957.3a0000 0000 9255 8984Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, 140 Hanzhong Road, Nanjing, 210029 China
| | - Jiawen You
- grid.89957.3a0000 0000 9255 8984Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, 140 Hanzhong Road, Nanjing, 210029 China
| | - Panpan Zhao
- grid.89957.3a0000 0000 9255 8984Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, 140 Hanzhong Road, Nanjing, 210029 China
| | - Weiguang Liu
- grid.260474.30000 0001 0089 5711Department of Biochemistry, School of Life Sciences, Nanjing Normal University, Nanjing, 210023 China
| | - Na Zhao
- grid.89957.3a0000 0000 9255 8984Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, 140 Hanzhong Road, Nanjing, 210029 China
| | - Zhichun Yu
- Green Hope High School, Cary, NC 27519 USA
| | - Junqing Ma
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, 140 Hanzhong Road, Nanjing, 210029, China. .,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, 210029, China.
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Xiang C, Ni H, Wang Z, Ji B, Wang B, Shi X, Wu W, Liu N, Gu Y, Ma D, Liu H. Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis. Front Genet 2021; 12:756784. [PMID: 34721544 PMCID: PMC8551569 DOI: 10.3389/fgene.2021.756784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 09/24/2021] [Indexed: 12/18/2022] Open
Abstract
Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.
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Affiliation(s)
- Chenxi Xiang
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Huimin Ni
- Department of Pathology, Xuzhou Medical University, Xuzhou, China
| | - Zhina Wang
- Department of Oncology, Emergency General Hospital, Beijing, China
| | - Binbin Ji
- Genies Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Bo Wang
- Genies Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoli Shi
- Genies Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Wanna Wu
- Department of Pathology, Xuzhou Medical University, Xuzhou, China
| | - Nian Liu
- Department of Pathology, Xuzhou Medical University, Xuzhou, China
| | - Ying Gu
- Department of Pathology, Xuzhou Medical University, Xuzhou, China
| | - Dongshen Ma
- Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hui Liu
- Department of Pathology, Xuzhou Medical University, Xuzhou, China
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5
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Koo B, Rhee JK. Prediction of tumor purity from gene expression data using machine learning. Brief Bioinform 2021; 22:6265216. [PMID: 33954576 DOI: 10.1093/bib/bbab163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 01/11/2023] Open
Abstract
MOTIVATION Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity. RESULTS We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system. AVAILABILITY The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.
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Affiliation(s)
- Bonil Koo
- School of Systems Biomedical Science, Soongsil University, Seoul, Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Je-Keun Rhee
- School of Systems Biomedical Science, Soongsil University, Seoul, Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
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6
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Li X, Liu L, Goodall GJ, Schreiber A, Xu T, Li J, Le TD. A novel single-cell based method for breast cancer prognosis. PLoS Comput Biol 2020; 16:e1008133. [PMID: 32833968 PMCID: PMC7470419 DOI: 10.1371/journal.pcbi.1008133] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 09/03/2020] [Accepted: 07/09/2020] [Indexed: 12/12/2022] Open
Abstract
Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.
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Affiliation(s)
- Xiaomei Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Gregory J. Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA, Australia
- School of Medicine, Discipline of Medicine, University of Adelaide, SA, Australia
| | - Andreas Schreiber
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA, Australia
| | - Taosheng Xu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Thuc D. Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
- * E-mail:
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7
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A novel matched-pairs feature selection method considering with tumor purity for differential gene expression analyses. Math Biosci 2019; 311:39-48. [DOI: 10.1016/j.mbs.2019.02.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 12/13/2022]
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Ng JCF, Quist J, Grigoriadis A, Malim MH, Fraternali F. Pan-cancer transcriptomic analysis dissects immune and proliferative functions of APOBEC3 cytidine deaminases. Nucleic Acids Res 2019; 47:1178-1194. [PMID: 30624727 PMCID: PMC6379723 DOI: 10.1093/nar/gky1316] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 12/19/2018] [Accepted: 01/04/2019] [Indexed: 12/25/2022] Open
Abstract
APOBEC3 cytidine deaminases are largely known for their innate immune protection from viral infections. Recently, members of the family have been associated with a distinct mutational activity in some cancer types. We report a pan-tissue, pan-cancer analysis of RNA-seq data specific to the APOBEC3 genes in 8,951 tumours, 786 cancer cell lines and 6,119 normal tissues. By deconvolution of levels of different cell types in tumour admixtures, we demonstrate that APOBEC3B (A3B), the primary candidate as a cancer mutagen, shows little association with immune cell types compared to its paralogues. We present a pipeline called RESPECTEx (REconstituting SPecific Cell-Type Expression) and use it to deconvolute cell-type specific expression levels in a given cohort of tumour samples. We functionally annotate APOBEC3 co-expressing genes, and create an interactive visualization tool which 'barcodes' the functional enrichment (http://fraternalilab.kcl.ac.uk/apobec-barcodes/). These analyses reveal that A3B expression correlates with cell cycle and DNA repair genes, whereas the other APOBEC3 members display specificity for immune processes and immune cell populations. We offer molecular insights into the functions of individual APOBEC3 proteins in antiviral and proliferative contexts, and demonstrate the diversification this family of enzymes displays at the transcriptomic level, despite their high similarity in protein sequences and structures.
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Affiliation(s)
- Joseph C F Ng
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | - Jelmar Quist
- Cancer Bioinformatics, School of Cancer and Pharmaceutical Sciences, CRUK King's Health Partners Centre, Breast Cancer Now Research Unit, King's College London, London, UK
| | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer and Pharmaceutical Sciences, CRUK King's Health Partners Centre, Breast Cancer Now Research Unit, King's College London, London, UK
| | - Michael H Malim
- Department of Infectious Diseases, School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
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