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Zhao L, He S, Liu Z, Song Z, Hou X, Gai L. Bioinformatics analysis of the prognostic role of alternative splicing data in lung adenocarcinoma. J Thorac Dis 2024; 16:1463-1472. [PMID: 38505068 PMCID: PMC10944774 DOI: 10.21037/jtd-24-6] [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: 01/02/2024] [Accepted: 02/04/2024] [Indexed: 03/21/2024]
Abstract
Background As a post-transcriptional regulatory mechanism, alternative splicing (AS) is engaged in a variety of pathophysiological processes, and it has been widely reported in connection with the occurrence, progression, metastasis, and drug resistance of cancer. However, the research on AS in lung adenocarcinoma (LUAD) is very limited. In addition, the prognostic effect of AS event (ASE) on LUAD and its related mechanism are not clear. This study aimed to explore the role and potential prognostic value of ASE in LUAD. Methods Relevant data and ASE datasets of the sample were acquired from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. We constructed a new prognostic criterion based on ASEs. Then, Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the model. Based on this model, the risk score of each ASE was calculated, and the reliability of this model was evaluated by Kaplan-Meier survival and receiver operating characteristic (ROC) curve analyses. Finally, these results were verified on different network platforms. Results We identified seven types of ASEs related to survival. The prognostic risk model for ASEs was established. The Kaplan-Meier curve showed that compared to the low-risk group, the overall survival (OS) rate of LUAD patients in the high-risk group was lower. ROC curve analysis showed that the prognostic risk model of LUAD patients was well predicted, and the area under the curve (AUC) also confirmed this. Conclusions This study screened the ASE related to the prognosis of LUAD patients, and provided a theoretical basis for further study of the correlation between ASE and the prognosis of LUAD patients. It has provided new ideas for developing new biomarkers and therapeutic targets for LUAD patients.
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Affiliation(s)
- Lingling Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Nantong University, The First People’s Hospital of Nantong, Nantong, China
| | - Shuting He
- Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong, China
- Department of Oncology, Medical School of Nantong University, Nantong, China
| | - Ziwei Liu
- Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong, China
- Department of Oncology, Medical School of Nantong University, Nantong, China
| | - Zhibin Song
- Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong, China
- Department of Oncology, Medical School of Nantong University, Nantong, China
| | - Xiaochun Hou
- Department of Oncology, The Second People’s Hospital of Nantong, Nantong, China
| | - Ling Gai
- Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong, China
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Xie JQ, Zhou X, Jia ZC, Su CF, Zhang Y, Fernie AR, Zhang J, Du ZY, Chen MX. Alternative Splicing, An Overlooked Defense Frontier of Plants with Respect to Bacterial Infection. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37916838 DOI: 10.1021/acs.jafc.3c04163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Disease represents a major problem in sustainable agricultural development. Plants interact closely with various microorganisms during their development and in response to the prevailing environment. In particular, pathogenic microorganisms can cause plant diseases, affecting the fertility, yield, and longevity of plants. During the long coevolution of plants and their pathogens, plants have evolved both molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) signaling networks in order to regulate host cells in response to pathogen infestation. Additionally, in the postgenomic era, alternative splicing (AS) has become uncovered as one of the major drivers of proteome diversity, and abnormal RNA splicing is closely associated with bacterial infections. Currently, the complexity of host-bacteria interactions is a much studied area of research that has shown steady progress over the past decade. Although the development of high-throughput sequencing technologies and their application in transcriptomes have revolutionized our understanding of AS, many mechanisms related to host-bacteria interactions remain still unclear. To this end, this review summarizes the changes observed in AS during host-bacteria interactions and outlines potential therapeutics for bacterial diseases based on existing studies. In doing so, we hope to provide guidelines for plant disease management in agriculture.
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Affiliation(s)
- Ji-Qin Xie
- Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences/Institute of Agro-bioengineering, Guizhou University, Guiyang, Guizhou 550025, China
| | - Xiang Zhou
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang 550025, China
| | - Zi-Chang Jia
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang 550025, China
| | - Chang-Feng Su
- Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences/Institute of Agro-bioengineering, Guizhou University, Guiyang, Guizhou 550025, China
| | - Youjun Zhang
- Center of Plant System Biology and Biotechnology, 4000 Plovdiv, Bulgaria
- Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Golm, Germany
| | - Alisdair R Fernie
- Center of Plant System Biology and Biotechnology, 4000 Plovdiv, Bulgaria
- Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Golm, Germany
| | - Jianhua Zhang
- Department of Biology, Hong Kong Baptist University, and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Zhi-Yan Du
- Department of Molecular Biosciences & Bioengineering, University of Hawaii at Manoa, Honolulu, Hawaii 96822, United States
| | - Mo-Xian Chen
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang 550025, China
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Hong Z, Chen X, Wang L, Zhou X, He H, Zou G, Liu Q, Wang Y. ROCK2-RNA interaction map reveals multiple biological mechanisms underlying tumor progression in renal cell carcinoma. Hum Cell 2023; 36:1790-1803. [PMID: 37418232 DOI: 10.1007/s13577-023-00947-x] [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: 03/12/2023] [Accepted: 06/24/2023] [Indexed: 07/08/2023]
Abstract
Renal cell carcinoma (RCC) is the most common form of kidney cancer in adults. Despite new therapeutic modalities, the outcomes for RCC patients remain unsatisfactory. Rho-associated coiled-coil forming protein kinase 2 (ROCK2) has previously been shown to be upregulated in RCC, and its expression was negatively correlated with patient survival. However, the precise molecular function of ROCK2 has remained unclear. Herein, using RNA-seq analysis of ROCK2 knockdown and control cells, we identified 464 differentially expressed genes, and 1287 alternative splicing events in 786-O RCC cells. Furthermore, mapping of iRIP-seq reads in 786-O cells showed a biased distribution at 5' UTR, intronic and intergenic regions. By comparing ROCK2-regulated alternative splicing and iRIP-seq data, we found 292 overlapping genes that are enriched in multiple tumorigenic pathways. Taken together, our work defined a complex ROCK2-RNA interaction map on a genomic scale in a human RCC cell line, which deepens our understanding of the molecular function of ROCK2 in cancer development.
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Affiliation(s)
- Zhengdong Hong
- Department of Urology Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xuexin Chen
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, 511436, China
| | - Lei Wang
- School of Pharmacy, Nanchang Medical College, Nanchang, China
- Jiangxi Health Vocational College, Nanchang, China
| | - Xiaocheng Zhou
- Department of Urology Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Haowei He
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, 511436, China
| | - Gaode Zou
- Department of Urology Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qingnan Liu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, 511436, China.
| | - Yiqian Wang
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, 511436, China.
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Jiang Y, Zhang C, Chen Y, Zhao S, He Y, He J. Prognostic risk assessment model for alternative splicing events and splicing factors in malignant pleural mesothelioma. Cancer Med 2023; 12:4895-4906. [PMID: 36031798 PMCID: PMC9972025 DOI: 10.1002/cam4.5174] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Malignant pleural mesothelioma (MPM) is a rare and highly malignant thoracic tumor. Although alternative splicing (AS) is associated with tumor prognosis, the prognostic significance of AS in MPM is unknown. METHODS Transcriptomic data, clinical information, and splicing percentage values for MPM were obtained from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq databases. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analyses were performed to establish a model affecting the prognosis of MPM. Survival and ROC analyses were used to test the effects of the prognostic model. LASSO/multivariate Cox analysis was used to construct the MPM prognostic splicing factor (SF) model. The SF-AS interaction network was analyzed using Spearman correlation and visualized using Cytoscape. The association between the MPM prognostic SF model and drug sensitivity to chemotherapeutic agents such as cisplatin was analyzed using pRRophetic.R. RESULTS The LASSO/multivariate Cox analysis identified 41 AS events and 2 SFs that were mostly associated with survival. Nine prognostic prediction models (i.e., seven types of AS model, total AS model, and SF model) were developed. An MPM prognostic SF-AS regulatory network was subsequently constructed with decreased drug sensitivity in the SF model high-risk group (p = 0.025). CONCLUSION This study provides the first comprehensive analysis of the prognostic value of AS events and SFs in MPM. The SF-AS regulatory network established in this study and our drug sensitivity analysis using the SF model may provide novel targets for pharmacological studies of MPM.
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Affiliation(s)
- Yue Jiang
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Chengda Zhang
- Department of Gastroenterology, The Third Hospital of Mian Yang (Sichuan Mental Health Center), Mianyang, China
| | - Yang Chen
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Shiyu Zhao
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Yipeng He
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Jun He
- Department of Oncology, The Third Hospital of Mian Yang (Sichuan Mental Health Center), Mianyang, China
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AS-CMC: a pan-cancer database of alternative splicing for molecular classification of cancer. Sci Rep 2022; 12:21074. [PMID: 36473963 PMCID: PMC9726986 DOI: 10.1038/s41598-022-25584-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Alternative splicing (AS) is a post-transcriptional regulation that leads to the complexity of the transcriptome. Despite the growing importance of AS in cancer research, the role of AS has not been systematically studied, especially in understanding cancer molecular classification. Herein, we analyzed the molecular subtype-specific regulation of AS using The Cancer Genome Atlas data and constructed a web-based database, named Alternative Splicing for Cancer Molecular Classification (AS-CMC). Our system harbors three analysis modules for exploring subtype-specific AS events, evaluating their phenotype association, and performing pan-cancer comparison. The number of subtype-specific AS events was found to be diverse across cancer types, and some differentially regulated AS events were recurrently found in multiple cancer types. We analyzed a subtype-specific AS in exon 11 of mitogen-activated protein kinase kinase 7 (MAP3K7) as an example of a pan-cancer AS biomarker. This AS marker showed significant association with the survival of patients with stomach adenocarcinoma. Our analysis revealed AS as an important determinant for cancer molecular classification. AS-CMC is the first web-based resource that provides a comprehensive tool to explore the biological implications of AS events, facilitating the discovery of novel AS biomarkers.
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Casuscelli J, Pal SK. Aberrant Splice Variants: A Novel Characterization of Clear Cell Renal Cell Carcinoma. Eur Urol 2022; 82:363-364. [DOI: 10.1016/j.eururo.2022.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/17/2022] [Indexed: 11/26/2022]
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Kang L, Dai C, Wang L, Pan X. Potential biomarkers that discriminate rheumatoid arthritis and osteoarthritis based on the analysis and validation of datasets. BMC Musculoskelet Disord 2022; 23:319. [PMID: 35379209 PMCID: PMC8978354 DOI: 10.1186/s12891-022-05277-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 03/28/2022] [Indexed: 02/05/2023] Open
Abstract
Background Rheumatoid arthritis (RA) and osteoarthritis (OA) share some similar arthritic symptoms, but different mechanisms underlie the pathogenesis of these two diseases. Analysis of differentially expressed molecules in rheumatoid arthritis and osteoarthritis may assist in improving diagnosis and treatment strategies in clinical practice. Methods Microarray and RNA-seq data were acquired from the gene expression omnibus database. Differentially expressed genes (DEGs) were identified using Bioconductor packages. Receiver operating characteristic curves were plotted to assess performance. Gene ontology enrichment analysis was conducted using the clusterProfiler application. During validation, synovial fluid was harvested from patients who had undergone in-hospital joint replacement, in which the expression of proteins was measured using enzyme-linked immunosorbent assays. Results Compared with OA samples, RA samples showed 14 genes to be upregulated and 3 to be downregulated. Gene ontology analysis indicated that DEGs principally included molecules responsible for the regulation of a synovial tissue inflammatory response. Seven genes displayed a good discriminatory power with an AUC higher than 0.90. ADAMDEC1 was the biomarker that most clearly discriminated RA from OA in the database, exhibiting an AUC of 0.999, a sensitivity of 100%, and a specificity of 97.8%. Following validation, the expression levels of ADAMDEC1 in the synovial fluid from RA patients were significantly higher than those in the synovial fluid from OA patients (P < 0.05). At the cut-off value of 1957 pg/mL, ADAMDEC1 expression in the synovial fluid discriminated RA from OA with an AUC of 0.951, a specificity of 88.6%, and a sensitivity of 92.9%. Conclusion The differential expression of genes in RA compared with OA indicates potential targets for molecular diagnosis and treatment. The presence of ADAMDEC1 in synovial fluid is a good biomarker of RA. Supplementary Information The online version contains supplementary material available at 10.1186/s12891-022-05277-x.
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Affiliation(s)
- Le Kang
- Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Chengqian Dai
- Department of Orthopedics, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Lihong Wang
- Department of Orthopedics, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Xinling Pan
- Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
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Marquardt A, Solimando AG, Kerscher A, Bittrich M, Kalogirou C, Kübler H, Rosenwald A, Bargou R, Kollmannsberger P, Schilling B, Meierjohann S, Krebs M. Subgroup-Independent Mapping of Renal Cell Carcinoma-Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries. Front Oncol 2021; 11:621278. [PMID: 33791209 PMCID: PMC8005734 DOI: 10.3389/fonc.2021.621278] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 02/15/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups. Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256). Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients. Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.
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Affiliation(s)
- André Marquardt
- Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, Würzburg, Germany.,Institute of Pathology, University of Würzburg, Würzburg, Germany.,Interdisciplinary Center for Clinical Research, University Hospital Würzburg, Würzburg, Germany
| | - Antonio Giovanni Solimando
- Guido Baccelli Unit of Internal Medicine, Department of Biomedical Sciences and Human Oncology, School of Medicine, Aldo Moro University of Bari, Bari, Italy.,IRCCS Istituto Tumori "Giovanni Paolo II" of Bari, Bari, Italy
| | - Alexander Kerscher
- Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, Würzburg, Germany
| | - Max Bittrich
- Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Charis Kalogirou
- Department of Urology and Pediatric Urology, University Hospital Würzburg, Würzburg, Germany
| | - Hubert Kübler
- Department of Urology and Pediatric Urology, University Hospital Würzburg, Würzburg, Germany
| | | | - Ralf Bargou
- Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, Würzburg, Germany
| | - Philip Kollmannsberger
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | - Bastian Schilling
- Department of Dermatology, University Hospital Würzburg, Würzburg, Germany
| | - Svenja Meierjohann
- Institute of Pathology, University of Würzburg, Würzburg, Germany.,Interdisciplinary Center for Clinical Research, University Hospital Würzburg, Würzburg, Germany
| | - Markus Krebs
- Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, Würzburg, Germany.,Department of Urology and Pediatric Urology, University Hospital Würzburg, Würzburg, Germany
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